{
  "metadata": {
    "worker_model": "qwen2.5-14b-instruct-mlx",
    "judge_model": "qwen3-30b-a3b-instruct-2507",
    "lm_studio_url": "http://10.16.68.184:1234/v1",
    "timestamp": "2026-02-22T16:16:41",
    "corpus_docs": 89,
    "query_count": 108
  },
  "results": {
    "langchain": [
      {
        "competitor": "langchain",
        "query": "What is semantic search?",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/semantic-search.md",
          "tests/fixtures/sample_docs/vector-databases.md",
          "tests/fixtures/sample_docs/document-processing.md",
          "tests/fixtures/sample_docs/semantic-search.md",
          "tests/fixtures/sample_docs/document-processing.md"
        ],
        "worker_answer": "Semantic search is a technique that goes beyond keyword matching to understand the meaning and intent behind queries, using embeddings and similarity search in high-dimensional space to find documents with similar meanings.",
        "reference_answer": "Semantic search is a method of information retrieval that goes beyond simple keyword matching to understand the meaning and intent behind queries. It involves techniques like query preprocessing to handle negation and expand queries with synonyms, as well as ranking and reranking approaches that consider metadata matches, recency, popularity, and other relevance signals. The goal of semantic search is to improve the accuracy and relevance of search results.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 484,
        "worker_latency_ms": 4732.433167053387,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "vector database",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/vector-databases.md",
          "tests/fixtures/sample_docs/vector-databases.md",
          "tests/fixtures/scale_docs/database-replication.md",
          "tests/fixtures/scale_docs/migration-strategies.md",
          "tests/fixtures/scale_docs/data-warehouse-modeling.md"
        ],
        "worker_answer": "Vector databases store and retrieve data based on semantic similarity using embeddings. Popular vector databases include ChromaDB, Pinecone, Weaviate, and Milvus.",
        "reference_answer": "A vector database is a type of database that stores and retrieves data based on semantic similarity rather than exact keyword matches. Vector databases use embeddings, which are numerical representations of text that capture semantic meaning, to represent documents as high-dimensional vectors. This allows the database to perform similarity search, where it finds documents with similar meanings by calculating distances between vectors in high-dimensional space. Common use cases for vector databases include semantic search engines, recommendation systems, question answering systems, and document clustering and classification.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 452,
        "worker_latency_ms": 4421.711166971363,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "REST API design",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/api-design.md",
          "tests/fixtures/scale_docs/api-security.md",
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/sample_docs/api-design.md"
        ],
        "worker_answer": "REST API design follows the principles of being stateless, resource-based, using standard HTTP methods and status codes. It emphasizes proper URL structure using nouns for resources, versioning in URLs or headers, and consistent error handling formats. Additionally, best practices include generating unique API keys for each consumer, implementing key rotation, enforcing rate limiting, and using HTTPS.",
        "reference_answer": "REST API design follows several key principles:\n\n- Stateless: Each request contains all the information needed to process it, without relying on server-side state.\n- Resource-based: URLs represent resources (nouns) rather than actions (verbs).\n- HTTP methods: Standard HTTP methods like GET, POST, PUT, and DELETE are used to perform CRUD operations on resources.\n- Standard status codes: APIs return consistent HTTP status codes like 200 OK, 404 Not Found, and 500 Server Error.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 406,
        "worker_latency_ms": 6346.678874921054,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "database normalization",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/database-design.md",
          "tests/fixtures/scale_docs/data-warehouse-modeling.md",
          "tests/fixtures/scale_docs/sql-query-optimization.md",
          "tests/fixtures/sample_docs/database-design.md",
          "tests/fixtures/scale_docs/nosql-patterns.md"
        ],
        "worker_answer": "Database normalization refers to the process of organizing data to reduce redundancy and improve integrity. It involves structuring tables in a way that adheres to specific normal forms, such as First Normal Form (1NF), Second Normal Form (2NF), and Third Normal Form (3NF). Each form imposes stricter rules to eliminate certain problems like repeating groups, partial dependencies, and transitive dependencies. Normalization typically leads to a more complex schema with multiple tables that need to be joined, which can improve data integrity and storage efficiency but may complicate queries.",
        "reference_answer": "Database normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. The key principles of database normalization are:\n\n1. First Normal Form (1NF): Ensure that all columns contain atomic values (no arrays or lists) and each column has a unique name.\n2. Second Normal Form (2NF): Ensure that all non-key columns are fully dependent on the primary key, with no partial dependencies.\n3. Third Normal Form (3NF): Ensure that non-key columns do not have transitive dependencies, meaning they do not depend on other non-key columns.\n\nAdhering to these normalization principles helps to create a well-structured, efficient, and maintainable database schema.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 419,
        "worker_latency_ms": 7817.23708298523,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "HTTP/2 vs HTTP/3 performance",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/http2-http3.md",
          "tests/fixtures/scale_docs/http2-http3.md",
          "tests/fixtures/scale_docs/http2-http3.md",
          "tests/fixtures/scale_docs/http2-http3.md",
          "tests/fixtures/scale_docs/http2-http3.md"
        ],
        "worker_answer": "The context does not provide a direct comparison of HTTP/2 vs HTTP/3 performance. It mentions that both protocols aim to improve performance but does not specify how they compare against each other in terms of performance metrics. I don't know.",
        "reference_answer": "HTTP/2 offers several performance improvements over HTTP/1.1, including:\n\nMultiplexing: HTTP/2 allows multiple requests and responses to be in flight simultaneously over a single TCP connection, reducing latency compared to the serial request-response model of HTTP/1.1.\n\nServer Push: HTTP/2 enables the server to proactively send resources to the client before they are requested, further improving performance by reducing round trips.\n\nHeader Compression: HTTP/2 compresses request and response headers, reducing the amount of data that needs to be transmitted.\n\nIn contrast, HTTP/3 is built on top of the QUIC protocol, which uses UDP instead of TCP. This allows for faster connection establishment and recovery from packet loss, potentially improving performance in certain network conditions compared to HTTP/2.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 315,
        "worker_latency_ms": 4254.122291924432,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "langchain",
        "query": "PostgreSQL 15 internals",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/postgresql-internals.md",
          "tests/fixtures/scale_docs/database-replication.md",
          "tests/fixtures/scale_docs/database-backup-recovery.md",
          "tests/fixtures/scale_docs/connection-pooling.md",
          "tests/fixtures/scale_docs/postgresql-internals.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain specific information about PostgreSQL 15 internals. It covers some aspects of PostgreSQL configurations, replication using Pglogical, `pg_dump`, and the `VACUUM` process but does not provide details about PostgreSQL 15 internals.",
        "reference_answer": "PostgreSQL 15 uses a Multiversion Concurrency Control (MVCC) system to manage concurrent transactions and ensure data consistency. MVCC allows multiple transactions to access the same data simultaneously without causing conflicts or data corruption. PostgreSQL also uses a Write-Ahead Logging (WAL) system to ensure data durability and consistency, where changes are first recorded in the WAL before being applied to the database.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 370,
        "worker_latency_ms": 5601.370792021044,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "Apache Kafka 3.0 features",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/apache-kafka.md",
          "tests/fixtures/scale_docs/apache-kafka.md",
          "tests/fixtures/scale_docs/apache-kafka.md",
          "tests/fixtures/scale_docs/apache-kafka.md",
          "tests/fixtures/scale_docs/data-pipeline-design.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "Apache Kafka 3.0 introduced several new features:\n\n1. Improved Kafka Streams API: Kafka Streams 3.0 includes enhancements to the Kafka Streams API, such as improved support for stateful processing and new operators for data transformation and aggregation.\n\n2. Enhanced Kafka Connect: Kafka Connect 3.0 includes improvements to the Kafka Connect framework, including better support for data transformation and improved error handling.\n\n3. Expanded Kafka Security: Kafka 3.0 includes expanded security features, such as support for SASL/SCRAM authentication and improved integration with external authentication providers.\n\n4. Improved Kafka Monitoring: Kafka 3.0 includes enhancements to the Kafka monitoring and metrics system, including new metrics and improved visualization tools.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 387,
        "worker_latency_ms": 2717.8101249737665,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "TLS 1.3 handshake process",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/tls-ssl.md",
          "tests/fixtures/scale_docs/tls-ssl.md",
          "tests/fixtures/scale_docs/websocket-protocol.md",
          "tests/fixtures/scale_docs/tls-ssl.md",
          "tests/fixtures/scale_docs/tls-ssl.md"
        ],
        "worker_answer": "I don't know.\nThe provided context does not contain information about the TLS 1.3 handshake process specifically. It describes parts of a standard TLS/SSL handshake and mTLS, but does not detail the TLS 1.3 process.",
        "reference_answer": "The TLS 1.3 handshake process involves the following key steps:\n\n1. Client Hello: The client sends a message to the server that includes the TLS version, a list of supported cipher suites, compression methods, and a random value.\n\n2. Server Hello: The server responds with a message that includes the selected TLS version, cipher suite, compression method, and a random value.\n\n3. Server Certificate: The server sends its X.509 certificate chain to the client, including the server's public key certificate, any intermediate CA certificates, and the root CA certificate.\n\n4. Client Key Exchange: The client generates a pre-master secret and encrypts it with the server's public key from the certificate, then sends this encrypted pre-master secret to the server.\n\n5. Change Cipher Spec: Both the client and server send messages to indicate that all future communications will be encrypted using the negotiated cipher suite and keys.\n\n6. Finished: The client and server exchange encrypted \"Finished\" messages to confirm the successful completion of the handshake.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 1,
          "combined": 0.4286
        },
        "tokens_in_context": 487,
        "worker_latency_ms": 5635.178750031628,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "Kubernetes v1.24 removal of dockershim",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/kubernetes-architecture.md",
          "tests/fixtures/scale_docs/kubernetes-architecture.md",
          "tests/fixtures/scale_docs/kubernetes-architecture.md",
          "tests/fixtures/scale_docs/kubernetes-architecture.md",
          "tests/fixtures/scale_docs/kubernetes-architecture.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "Kubernetes v1.24 will remove the dockershim, which was a component that provided compatibility between Kubernetes and the Docker container runtime. With this removal, Kubernetes will no longer support the Docker runtime directly, and users will need to use alternative container runtimes like containerd or CRI-O. This change is part of Kubernetes' effort to decouple the container runtime from the core Kubernetes components, allowing for more flexibility and innovation in the container runtime ecosystem.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 395,
        "worker_latency_ms": 2901.0836670640856,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "langchain",
        "query": "gRPC over HTTP/2",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/grpc-protocol.md",
          "tests/fixtures/scale_docs/grpc-protocol.md",
          "tests/fixtures/scale_docs/http2-http3.md",
          "tests/fixtures/scale_docs/http2-http3.md",
          "tests/fixtures/scale_docs/grpc-protocol.md"
        ],
        "worker_answer": "gRPC uses HTTP/2 as the underlying transport protocol. This allows for efficient and fast communication between client and server, supporting various streaming types including unary calls, server streaming, client streaming, and bidirectional streaming. The protocol buffers handle the serialization and deserialization of data on both client and server stubs, while HTTP/2 manages the low-level networking details.",
        "reference_answer": "gRPC is a modern open-source remote procedure call (RPC) framework that uses HTTP/2 as the underlying transport protocol. The key features of gRPC over HTTP/2 are:\n\n1. Efficient binary serialization using Protocol Buffers (protobuf) for data exchange.\n2. Bidirectional streaming support, allowing for efficient real-time communication between clients and servers.\n3. Automatic generation of client and server stubs from protobuf service definitions, simplifying application development.\n4. Support for deadlines and cancellation, enabling robust and responsive distributed systems.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 410,
        "worker_latency_ms": 6094.476582948118,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "Redis 7.0 Multi-part AOF",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/redis-data-structures.md",
          "tests/fixtures/scale_docs/redis-data-structures.md",
          "tests/fixtures/scale_docs/redis-data-structures.md",
          "tests/fixtures/scale_docs/database-sharding.md",
          "tests/fixtures/scale_docs/container-fundamentals.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "Redis 7.0 introduced a new feature called \"Multi-part AOF\", which enhances the Append-Only File (AOF) persistence model. The Multi-part AOF allows the AOF file to be split into multiple files, improving the performance and reliability of the AOF persistence. This feature enables more efficient handling of large AOF files, as the Redis server can load and rewrite the AOF in a more scalable manner.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 278,
        "worker_latency_ms": 2301.368792075664,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "Elasticsearch 8.x security changes",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/linux-logging.md",
          "tests/fixtures/scale_docs/elasticsearch-basics.md",
          "tests/fixtures/scale_docs/elasticsearch-basics.md",
          "tests/fixtures/scale_docs/secrets-management.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "Elasticsearch 8.x introduced several security changes:\n\n1. Mandatory TLS encryption: Elasticsearch 8.x requires TLS encryption for all HTTP and transport connections by default. This enhances the security of data transmission.\n\n2. Removal of basic authentication: Elasticsearch 8.x no longer supports basic authentication. Users must now authenticate using token-based or PKI-based authentication methods.\n\n3. Deprecation of the `elasticsearch-users` file: The `elasticsearch-users` file for managing user credentials has been deprecated in favor of the new Elasticsearch security plugin.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 385,
        "worker_latency_ms": 3329.458582913503,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "OWASP Top 10 2021",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/owasp-top-10.md",
          "tests/fixtures/scale_docs/owasp-top-10.md",
          "tests/fixtures/scale_docs/nosql-patterns.md",
          "tests/fixtures/scale_docs/elasticsearch-basics.md",
          "tests/fixtures/scale_docs/infrastructure-as-code.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The OWASP Top 10 2021 is a list of the most critical security risks to web applications, as identified by the Open Web Application Security Project (OWASP). The top vulnerabilities include injection flaws (such as SQL injection and OS command injection), broken authentication, cross-site scripting (XSS), security misconfiguration, and insecure deserialization. To prevent these vulnerabilities, the document recommends techniques like input validation, using parameterized queries, applying the principle of least privilege, and leveraging web application firewalls and dynamic query builders.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 344,
        "worker_latency_ms": 2866.683332948014,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "AVX-512 vector instructions",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/systemd-services.md",
          "tests/fixtures/scale_docs/spark-fundamentals.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/websocket-protocol.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 0,
          "combined": 0.0
        },
        "tokens_in_context": 370,
        "worker_latency_ms": 3264.247459010221,
        "judge_raw_response": "no reference answer"
      },
      {
        "competitor": "langchain",
        "query": "TCP/IP layer 4",
        "category": "lexical",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md",
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md",
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md",
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md",
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md"
        ],
        "worker_answer": "Layer 4 of the TCP/IP model is the Transport Layer, which includes protocols like TCP (Transmission Control Protocol) and UDP (User Datagram Protocol).",
        "reference_answer": "The Transport Layer in the TCP/IP protocol stack corresponds to layer 4 of the OSI model. This layer is responsible for ensuring complete data transfer between applications, including segmentation, flow control, and error correction. The two main protocols at the Transport Layer are TCP (Transmission Control Protocol) and UDP (User Datagram Protocol). TCP provides reliable, connection-oriented data transfer, while UDP offers unreliable, connectionless data transfer.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 287,
        "worker_latency_ms": 3313.829999999143,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "IPv6 address format",
        "category": "lexical",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md",
          "tests/fixtures/scale_docs/cloud-networking.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md",
          "tests/fixtures/scale_docs/linux-networking-config.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "An IPv6 address is a 128-bit address used to identify devices on an IPv6 network. The format of an IPv6 address consists of eight groups of four hexadecimal digits separated by colons, such as `2001:0db8:85a3:0000:0000:8a2e:0370:7334`. The first 64 bits represent the network prefix, and the last 64 bits represent the interface identifier, which is typically derived from the device's MAC address.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 193,
        "worker_latency_ms": 2279.4317920925096,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "UUID v4 generation",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/linux-filesystem.md",
          "tests/fixtures/scale_docs/secrets-management.md",
          "tests/fixtures/scale_docs/kubernetes-architecture.md",
          "tests/fixtures/scale_docs/testing-strategies.md",
          "tests/fixtures/scale_docs/data-quality.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "UUID v4 is a type of Universally Unique Identifier (UUID) that is randomly generated. The UUID v4 specification defines a method for generating UUIDs from random or pseudo-random numbers. This method produces a 128-bit number that is very likely to be unique across space and time. UUIDs generated using this method do not require a central registration authority and can be generated without consultation with that authority.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 324,
        "worker_latency_ms": 2744.0964580746368,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "0xEF hex code",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cryptography-basics.md",
          "tests/fixtures/scale_docs/linux-filesystem.md",
          "tests/fixtures/scale_docs/api-security.md",
          "tests/fixtures/scale_docs/websocket-protocol.md",
          "tests/fixtures/scale_docs/cryptography-basics.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The hexadecimal code 0xEF does not have a specific meaning in the context of cryptography. Hexadecimal codes are used to represent binary data, but 0xEF is not a commonly used value in cryptographic algorithms or standards. The source text does not mention the hexadecimal code 0xEF or provide any information about its meaning or usage in cryptography.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 419,
        "worker_latency_ms": 3926.025375025347,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "Python 3.10 match statement",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/functional-programming.md",
          "tests/fixtures/scale_docs/shell-scripting.md",
          "tests/fixtures/scale_docs/error-handling-patterns.md",
          "tests/fixtures/scale_docs/shell-scripting.md",
          "tests/fixtures/scale_docs/shell-scripting.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The Python 3.10 match statement is a new pattern matching feature introduced in Python 3.10. It allows you to match a value against a series of patterns, similar to a switch statement in other programming languages. The match statement provides a concise and expressive way to handle complex data structures and control flow in your Python code.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 370,
        "worker_latency_ms": 3106.4523750683293,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "ISO 27001 compliance",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/compliance-frameworks.md",
          "tests/fixtures/scale_docs/compliance-frameworks.md",
          "tests/fixtures/scale_docs/compliance-frameworks.md",
          "tests/fixtures/scale_docs/compliance-frameworks.md",
          "tests/fixtures/scale_docs/compliance-frameworks.md"
        ],
        "worker_answer": "I don't know.\n\nThe provided context does not specify what ISO 27001 compliance means independently of certification. It only mentions the requirements and process for obtaining ISO 27001 certification, not compliance.",
        "reference_answer": "ISO 27001 is an international standard that specifies the requirements for an information security management system (ISMS). To achieve ISO 27001 compliance, organizations must implement a comprehensive set of security controls across 14 domains, including information security policies, access control, cryptography, physical security, operations security, and incident management. The standard requires organizations to conduct regular risk assessments, implement appropriate security measures, and undergo third-party audits to verify the effectiveness of their ISMS.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 281,
        "worker_latency_ms": 4276.544125052169,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "langchain",
        "query": "SOC2 Type II audit",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/compliance-frameworks.md",
          "tests/fixtures/scale_docs/compliance-frameworks.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md"
        ],
        "worker_answer": "SOC 2 Type 2 evaluates the design and operating effectiveness of an organization's controls over a period of time (typically 6-12 months).",
        "reference_answer": "A SOC 2 Type II audit evaluates the design and operating effectiveness of an organization's controls related to security, availability, processing integrity, confidentiality, and privacy of data over a period of time, typically 6-12 months. To achieve SOC 2 compliance, organizations must implement controls across these 5 trust principles, including access management, logging and monitoring, change management, vulnerability management, incident response, backup and disaster recovery, risk assessment, vendor management, and security awareness training.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 220,
        "worker_latency_ms": 3186.805416014977,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "BGE-small-en-v1.5 model",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/kubernetes-architecture.md",
          "tests/fixtures/scale_docs/cost-optimization.md",
          "tests/fixtures/scale_docs/data-lake-architecture.md",
          "tests/fixtures/scale_docs/migration-strategies.md",
          "tests/fixtures/scale_docs/disaster-recovery.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The bge-small-en-v1.5 model is the embedding model used by the Candlekeep RAG (Retrieval-Augmented Generation) knowledge base server. Candlekeep uses this model as part of its \"Arcane Recall\" component within the RAG pipeline, which is responsible for the retrieval of relevant information from the underlying document database.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 413,
        "worker_latency_ms": 3347.04291599337,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "ChromaDB v0.4.0",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/package-management.md",
          "tests/fixtures/sample_docs/vector-databases.md",
          "tests/fixtures/scale_docs/data-lake-architecture.md",
          "tests/fixtures/scale_docs/package-management.md",
          "tests/fixtures/scale_docs/database-replication.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "ChromaDB v0.4.0 is not mentioned in the given source document. The document discusses the design of the Candlekeep system, which provides a RAG knowledge base that an AI agent can query. It describes the design goals and decisions, such as having three search paths (simple, hybrid, and precise), having the agent decompose queries rather than the search tool, and using \"Arcane Recall\" to return adjacent chunks around each match. However, there is no information about the specific ChromaDB version 0.4.0.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 336,
        "worker_latency_ms": 3297.111582942307,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "MS-MARCO MiniLM L-6-v2",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/database-sharding.md",
          "tests/fixtures/scale_docs/websocket-protocol.md",
          "tests/fixtures/scale_docs/secrets-management.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The MS-MARCO MiniLM L-6-v2 is a technique that is part of the \"Recommended Stack\" for the RAG (Retrieval Augmented Generation) system optimization project described in the source document. Specifically, the document states that the \"Tier 1 (Always Use)\" recommendation is to use the \"Bardic Knowledge\" and \"Arcane Recall\" techniques, which together achieve a precision of 97.3%, content quality of 88.7%, and latency of 37ms. The \"Tier 2 (Conditional)\" recommendation is to use the \"Flurry of Blows\" technique for complex queries, if the additional latency of around 1100ms is acceptable.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 499,
        "worker_latency_ms": 4127.1536659915,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "JWT RS256 signing",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/api-gateway-patterns.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/tls-ssl.md",
          "tests/fixtures/scale_docs/api-gateway-patterns.md",
          "tests/fixtures/sample_docs/authentication.md"
        ],
        "worker_answer": "The context does not provide specific information about JWT RS256 signing process or its implementation details. However, it does mention that the `authentication` configuration can use JWT with required claims and public-key verification. RS256 typically refers to using the RSA algorithm with SHA-256 for signing JWTs. Since RS256 is not directly mentioned, I can only infer that if the `verification` section uses a public key for JWT validation, it might imply support for RS256 signing. But without explicit mention of RS256, the direct answer to JWT RS256 signing based on provided context is:\n\nI don't know.",
        "reference_answer": "JWT (JSON Web Tokens) use the RS256 algorithm for signing. This is an asymmetric cryptographic algorithm that uses a private key to sign the token and a public key to verify it. The JWT structure consists of a header, payload, and signature. The header specifies the algorithm used, the payload contains the claims, and the signature is generated using the private key to ensure the integrity of the token. This allows for stateless authentication, where the server does not need to store session information.",
        "score": {
          "correctness": 2,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.4286
        },
        "tokens_in_context": 397,
        "worker_latency_ms": 10022.258582990617,
        "judge_raw_response": "{\"correctness\": 2, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "langchain",
        "query": "OAuth 2.0 PKCE flow",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/api-security.md",
          "tests/fixtures/scale_docs/api-security.md",
          "tests/fixtures/scale_docs/api-security.md",
          "tests/fixtures/scale_docs/api-security.md",
          "tests/fixtures/scale_docs/api-security.md"
        ],
        "worker_answer": "The OAuth 2.0 PKCE (Proof Key for Code Exchange) flow is an extension to the Authorization Code flow designed specifically for public clients. It involves generating a \"code verifier\" and deriving a \"code challenge\". The client sends the code challenge to the authorization server during the request for an authorization code and then uses the original code verifier when exchanging that code for an access token. This process helps mitigate security risks associated with public clients not being able to securely store their client secrets.",
        "reference_answer": "The OAuth 2.0 PKCE (Proof Key for Code Exchange) flow is a security enhancement to the standard OAuth 2.0 authorization code grant flow. It is designed to protect against authorization code interception attacks, particularly in public clients like mobile apps or single-page applications (SPAs).\n\nIn the PKCE flow, the client application generates a cryptographically random \"code verifier\" value and derives a \"code challenge\" from it. The client sends the code challenge to the authorization server when requesting an authorization code. When the client later exchanges the authorization code for an access token, it also sends the original code verifier. The authorization server verifies that the code verifier matches the previously sent code challenge, ensuring that the token exchange is performed by the same client that initiated the authorization request.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 433,
        "worker_latency_ms": 7647.187542053871,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "RAID 6 configurations",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cloud-storage-options.md",
          "tests/fixtures/scale_docs/database-replication.md",
          "tests/fixtures/scale_docs/linux-filesystem.md",
          "tests/fixtures/scale_docs/disaster-recovery.md",
          "tests/fixtures/scale_docs/cloud-storage-options.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "RAID 6 configurations provide data redundancy by using two independent parity blocks. This allows the system to continue operating even if two disks fail simultaneously. RAID 6 can tolerate the loss of any two disks in the array without data loss, making it a robust storage solution for mission-critical applications.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 338,
        "worker_latency_ms": 2858.187041943893,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "SQL-92 standards",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/scale_docs/data-pipeline-design.md",
          "tests/fixtures/scale_docs/data-quality.md",
          "tests/fixtures/scale_docs/data-quality.md",
          "tests/fixtures/scale_docs/postgresql-internals.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "SQL-92 refers to the SQL standard published in 1992, which was the third major revision of the SQL language. This standard introduced several key features, including:\n\n- Support for outer joins, which allow for the inclusion of rows that do not have matching values in the joined tables.\n- Improved support for subqueries, including the ability to use subqueries in the SELECT, FROM, and WHERE clauses.\n- Standardized syntax for common SQL operations, such as CREATE TABLE, ALTER TABLE, and DROP TABLE.\n- Improved support for data types, including the introduction of the TIMESTAMP data type.\n\nThe SQL-92 standard laid the foundation for the modern SQL language and is still widely used today, even as newer versions of the standard have been published.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 240,
        "worker_latency_ms": 2391.1167080514133,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "POSIX thread management",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/container-security.md"
        ],
        "worker_answer": "\"sched_get_priority_min\",\n                \"sched_getscheduler\",\n                \"sched_rr_get_interval\",\n                \"sched_setaffinity\",\n                \"sched_setattr\",\n                \"sched_setparam\",\n                \"sched_setscheduler\",\n                \"sched_yield\"",
        "reference_answer": "POSIX thread management in Linux is handled through the use of the `fork()` and `exec()` system calls. The `fork()` system call creates a new process, which is a copy of the calling process, and the `exec()` system call replaces the current process image with a new process image. Processes in Linux can be in one of several states, including running, waiting, stopped, and zombie. The current state of processes can be viewed using the `ps` command.",
        "score": {
          "correctness": 1,
          "completeness": 2,
          "hallucination": 0,
          "combined": 0.4286
        },
        "tokens_in_context": 604,
        "worker_latency_ms": 6378.541582962498,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 2, \"hallucination\": 0}"
      },
      {
        "competitor": "langchain",
        "query": "systemd unit files",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/systemd-services.md",
          "tests/fixtures/scale_docs/systemd-services.md",
          "tests/fixtures/scale_docs/systemd-services.md",
          "tests/fixtures/scale_docs/systemd-services.md",
          "tests/fixtures/scale_docs/systemd-services.md"
        ],
        "worker_answer": "Systemd uses \"unit files\" to manage services, sockets, mounts, devices, and other system resources. These configuration files define how a particular service or resource should be managed. Unit files are typically found in `/etc/systemd/system/` and may also appear in `/usr/lib/systemd/system/`. They have extensions like `.service`, `.socket`, `.target`, or others based on their type.",
        "reference_answer": "Systemd unit files are configuration files used by the systemd init system to manage services, sockets, mounts, devices, and other system resources on Linux. Unit files are typically located in the /etc/systemd/system/ and /usr/lib/systemd/system/ directories, and have extensions like .service, .socket, .target, or .timer depending on the type of unit.\n\nUnit files define how a particular service or resource should be managed, including the command to start/stop the service, dependencies on other units, and whether the service should be automatically restarted. Systemd also uses \"targets\" to group related services and resources together, similar to the runlevels used in older init systems.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 332,
        "worker_latency_ms": 6922.725250013173,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "EXT4 filesystem limits",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/linux-filesystem.md",
          "tests/fixtures/scale_docs/linux-filesystem.md",
          "tests/fixtures/scale_docs/linux-filesystem.md",
          "tests/fixtures/scale_docs/systemd-services.md",
          "tests/fixtures/scale_docs/linux-filesystem.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about specific limits of the EXT4 filesystem. It discusses features like extents, delayed allocation, and online defragmentation but does not mention any limits related to the filesystem itself.",
        "reference_answer": "The ext4 filesystem has the following limits:\n\n- Maximum file size: 16TB\n- Maximum filesystem size: 1EB (exabyte)\n- Maximum number of subdirectories within a directory: 64,000\n- Maximum number of hard links to a file: 65,000\n\nThese limits are defined by the ext4 filesystem design and cannot be changed after the filesystem is created.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 409,
        "worker_latency_ms": 5182.792917010374,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "YAML 1.2 syntax",
        "category": "lexical",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/api-gateway-patterns.md",
          "tests/fixtures/scale_docs/auto-scaling.md",
          "tests/fixtures/scale_docs/linux-logging.md",
          "tests/fixtures/scale_docs/configuration-management.md",
          "tests/fixtures/scale_docs/kubernetes-architecture.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "YAML 1.2 syntax is the version of the YAML data serialization format used by Ansible. Ansible Playbooks, which define the configuration management tasks to be executed, are written in YAML 1.2 format. The YAML syntax used in Ansible Playbooks follows a specific structure, including sections for hosts, tasks, handlers, variables, and templates. Ansible Playbooks are executed using the `ansible-playbook` command.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 486,
        "worker_latency_ms": 3983.602041960694,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "Docker Compose v2",
        "category": "lexical",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/container-fundamentals.md",
          "tests/fixtures/scale_docs/dependency-injection.md",
          "tests/fixtures/scale_docs/container-fundamentals.md",
          "tests/fixtures/scale_docs/kubernetes-architecture.md",
          "tests/fixtures/scale_docs/kubernetes-architecture.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "Docker Compose v2 is a newer version of the Docker Compose tool, which is used to define and run multi-container Docker applications. The key components that enable container technology, as described in the source text, are namespaces, control groups (cgroups), and union file systems (OverlayFS). These Linux kernel features provide isolation, resource management, and efficient layering of file systems for containers.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 476,
        "worker_latency_ms": 3720.053916913457,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "langchain",
        "query": "AWS IAM policies",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/secrets-management.md",
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/data-lake-architecture.md",
          "tests/fixtures/scale_docs/secrets-management.md",
          "tests/fixtures/scale_docs/well-architected-framework.md"
        ],
        "worker_answer": "IAM policies in AWS are used to grant or deny access to specific secrets or groups of secrets through fine-grained control. They can be used to implement security best practices, such as defining roles and policies that control access to resources. For example, an IAM policy can allow read access to a specific secret in Secrets Manager.",
        "reference_answer": "AWS IAM (Identity and Access Management) policies are JSON-based documents that define the permissions for AWS resources. These policies specify the actions that are allowed or denied on specific resources, such as Amazon S3 buckets, Amazon EC2 instances, or AWS Lambda functions. IAM policies can be attached to IAM users, groups, or roles to control their access to AWS services and resources. They are a fundamental component of AWS security and access management, allowing organizations to implement the principle of least privilege and grant only the necessary permissions to their AWS entities.",
        "score": {
          "correctness": 2,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.7143
        },
        "tokens_in_context": 408,
        "worker_latency_ms": 6339.899082900956,
        "judge_raw_response": "{\"correctness\": 2, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "how to implement chaos engineering",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/chaos-engineering.md",
          "tests/fixtures/scale_docs/chaos-engineering.md",
          "tests/fixtures/scale_docs/chaos-engineering.md",
          "tests/fixtures/scale_docs/chaos-engineering.md",
          "tests/fixtures/scale_docs/chaos-engineering.md"
        ],
        "worker_answer": "To implement chaos engineering, start by forming a hypothesis about the system's behavior under turbulent conditions. Use tools like the Chaos Toolkit to run experiments, such as increasing network latency for a specified duration (e.g., 10 seconds). Ensure there's a plan to roll back or recover from these experiments if they lead to unintended consequences. Evaluate the experiment results against a steady state hypothesis to determine if further investigation is needed.",
        "reference_answer": "To implement chaos engineering, you can follow these key principles and techniques:\n\n1. Conduct hypothesis-driven experiments to test the system's resilience under turbulent conditions. Clearly define a testable hypothesis about the system's behavior.\n\n2. Run chaos experiments continuously, not just during initial development or before major releases. Resilience must be an ongoing concern.\n\n3. Use controlled environments that closely mimic production to ensure representative results. Start with small, low-impact experiments and gradually increase the blast radius.\n\n4. Inject faults like network latency, resource exhaustion, service failures, and dependency failures to observe how the system responds. Tools like the Chaos Toolkit can help automate these fault injection experiments.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 335,
        "worker_latency_ms": 6232.326333061792,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "benefits of event-driven architecture",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/data-warehouse-modeling.md"
        ],
        "worker_answer": "The benefits of event-driven architecture include decoupling components for better scalability and resilience, allowing for asynchronous operations which improve responsiveness and non-blocking capabilities, and promoting a reactive programming style.",
        "reference_answer": "The key benefits of event-driven architecture include:\n\n1. Loose coupling: Components are decoupled, allowing for easier maintenance, independent scaling, and more flexibility.\n2. Asynchronous communication: Events can be processed asynchronously, improving responsiveness and scalability.\n3. Scalability: Individual components can scale independently due to the decoupled nature of the architecture.\n4. Fault tolerance: If one component fails, the rest of the system can continue to function, improving overall resilience.\n5. Audit trail: The event store provides a comprehensive record of all actions and state changes in the system.",
        "score": {
          "correctness": 2,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.7143
        },
        "tokens_in_context": 225,
        "worker_latency_ms": 3282.556083984673,
        "judge_raw_response": "{\"correctness\": 2, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "serverless vs containers",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/serverless-architecture.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain a comparison between serverless and containers.",
        "reference_answer": "Serverless architecture and containers are two different approaches to cloud computing. Serverless architecture, exemplified by AWS Lambda, is a cloud computing execution model where the cloud provider is responsible for executing code and managing the underlying infrastructure. In this model, developers focus on writing code without worrying about provisioning or managing servers. Containers, on the other hand, are a way to package and run applications in a consistent, isolated environment, allowing for portability and scalability across different computing environments.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 384,
        "worker_latency_ms": 2943.5662500327453,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "microservices data consistency",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md"
        ],
        "worker_answer": "Data consistency in microservices can be achieved through patterns like Saga for distributed transactions and Event Sourcing, which store events to rebuild state. These methods help manage consistency in a distributed environment where direct transactions across services are not feasible due to the service-oriented architecture.",
        "reference_answer": "",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 0,
          "combined": 0.0
        },
        "tokens_in_context": 355,
        "worker_latency_ms": 4484.286000020802,
        "judge_raw_response": "no reference answer"
      },
      {
        "competitor": "langchain",
        "query": "data lakehouse architecture",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-lake-architecture.md",
          "tests/fixtures/scale_docs/data-lake-architecture.md",
          "tests/fixtures/scale_docs/data-pipeline-design.md",
          "tests/fixtures/scale_docs/data-quality.md",
          "tests/fixtures/scale_docs/migration-strategies.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The data lakehouse architecture combines the flexibility and scalability of a data lake with the structured data management capabilities of a data warehouse. Key components include:\n\n1. Ingestion Layer: Responsible for bringing data into the data lake from various sources, using tools like Apache Kafka, Apache Spark Streaming, AWS Kinesis, and Azure Event Hubs.\n\n2. Storage Layer: The foundation of the data lake, where raw, unprocessed data is stored using object storage services like Amazon S3, Azure Blob Storage, or Google Cloud Storage.\n\n3. Processing Layer: Responsible for transforming and enriching the raw data stored in the data lake, using tools like Apache Spark, Apache Flink, AWS Glue, or Azure Databricks for batch and stream processing.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 0,
          "combined": 0.0
        },
        "tokens_in_context": 234,
        "worker_latency_ms": 1710.8109589898959,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 0}"
      },
      {
        "competitor": "langchain",
        "query": "zero trust security principles",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md"
        ],
        "worker_answer": "Zero trust security principles include continuous verification and validation of users, devices, and applications on a per-session, per-transaction basis based on dynamic risk assessment and policy enforcement. No component is automatically trusted; access is granted based on least privilege and real-time risk assessment.",
        "reference_answer": "The core principles of zero trust security include:\n\n1. Verify Explicitly: All users, devices, and applications must be continuously authenticated and authorized, regardless of network location.\n2. Use Least Privilege Access: Granular access policies should be defined to grant the minimum necessary privileges for each user, device, or application to perform their required functions.\n3. Assume Breach: Organizations should assume that a breach has already occurred and design security controls accordingly, focusing on data protection and rapid response.\n4. Verify and Monitor Everywhere: All network traffic, user activities, and system events should be continuously monitored and validated, both on-premises and in the cloud.\n\nImplementing a zero trust architecture involves key components such as identity verification and access control, micro-segmentation and least privilege, and continuous validation and monitoring.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 483,
        "worker_latency_ms": 5223.54366700165,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "continuous integration best practices",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/data-warehouse-modeling.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The key continuous integration best practices based on the source text are:\n\n1. Establish a clear CI/CD pipeline with well-defined stages, including source code management, build, test, static code analysis, artifact storage, and deployment.\n2. Automate the pipeline stages using tools like Jenkins, with specific steps for compiling the source code, running tests, analyzing the code, storing artifacts, and deploying the application.\n3. Manage the generated artifacts, such as compiled binaries, Docker images, and test reports, by storing them in a repository for later use.\n4. Implement parallel execution of pipeline stages to improve efficiency and reduce the overall build time.\n5. Leverage caching mechanisms to speed up the pipeline by reusing previously built artifacts or test results.\n6. Adopt deployment strategies like blue-green, canary, or rolling deployments to ensure reliable and safe application updates.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 85,
        "worker_latency_ms": 1120.5488329287618,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "database sharding strategies",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/database-sharding.md",
          "tests/fixtures/sample_docs/caching.md",
          "tests/fixtures/scale_docs/database-sharding.md",
          "tests/fixtures/scale_docs/database-sharding.md",
          "tests/fixtures/scale_docs/database-sharding.md"
        ],
        "worker_answer": "Database sharding strategies include techniques to partition data horizontally across multiple database instances, known as shards. These strategies aim to enhance scalability and performance by distributing data evenly, simplifying the addition or removal of shards, and maintaining transparency for applications treating the database as a single logical unit. However, cross-shard queries can be complex and may introduce latency, while materialized views require extra storage and maintenance.",
        "reference_answer": "Database sharding strategies refer to the different approaches used to horizontally partition data across multiple database instances, or \"shards\", to improve scalability and performance. The three main sharding strategies are:\n\n1. Hash-based sharding: This uses a hash function to determine the shard where a particular data item is stored, based on a specific column or set of columns. It results in even data distribution but can make cross-shard queries more complex.\n\n2. Range-based sharding: This partitions data based on a range of values in a specific column or set of columns, often used when data can be naturally divided into logical ranges. It can enable more efficient cross-shard queries but may lead to uneven data distribution.\n\n3. Directory-based sharding: This uses a central directory to map data to specific shards, allowing more flexibility in shard management but introducing additional complexity and a potential single point of failure.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 332,
        "worker_latency_ms": 5857.559084077366,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "langchain",
        "query": "load balancing algorithms",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/load-balancing.md",
          "tests/fixtures/scale_docs/load-balancing.md",
          "tests/fixtures/scale_docs/load-balancing.md",
          "tests/fixtures/scale_docs/load-balancing.md",
          "tests/fixtures/sample_docs/caching.md"
        ],
        "worker_answer": "Load balancing algorithms are the core logic that determines how incoming traffic is distributed across a pool of backend servers. The most common load balancing algorithms are not explicitly named in the provided context, but they are mentioned as being fundamental to modern distributed systems for achieving high availability, scalability, and fault tolerance.",
        "reference_answer": "The most common load balancing algorithms are:\n\nRound-robin: Sends each new connection to the next server in the list, cycling through the entire pool to ensure each server receives an equal number of connections.\n\nLeast connections: Sends each new connection to the server with the fewest number of active connections, helping to balance the load more evenly across backend servers.\n\nWeighted: Allows assigning a weight value to each backend server, so servers with a higher weight receive more traffic than those with a lower weight, useful for servers with different capacities.\n\nIP hash: Uses a hash of the client's IP address to determine which server the connection should be sent to, maintaining session affinity.\n\nLoad balancers can operate at different layers of the OSI model, with layer 4 load balancers making decisions based on transport layer information like IP addresses and ports, while layer 7 load balancers have more visibility into the application layer.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 296,
        "worker_latency_ms": 4493.2063749292865,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "langchain",
        "query": "content delivery network optimization",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cdn-architecture.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md",
          "tests/fixtures/scale_docs/migration-strategies.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md",
          "tests/fixtures/scale_docs/serverless-architecture.md"
        ],
        "worker_answer": "I don't know. The provided context does not specify techniques or strategies for optimizing a content delivery network (CDN) beyond mentioning that edge caching is crucial in CDN architecture.",
        "reference_answer": "A Content Delivery Network (CDN) is designed to efficiently deliver content to users around the world. The key components of a CDN architecture include edge servers, which are strategically placed to minimize the distance and latency between the user and the content, and an origin server, which is the primary source of the content. Load balancers direct user requests to the appropriate edge server, and a cache management system manages the content stored on the edge servers. Edge caching is a fundamental aspect of CDN architecture, where content is stored on the edge servers closest to the end-users, allowing the CDN to serve content directly from the edge and reduce latency and load on the origin server.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 1,
          "combined": 0.4286
        },
        "tokens_in_context": 83,
        "worker_latency_ms": 2507.2666249470785,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "service mesh advantages",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/service-mesh.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/data-serialization.md"
        ],
        "worker_answer": "I don't know. The provided context does not list the advantages of a service mesh architecture.",
        "reference_answer": "The key advantages of a service mesh architecture include:\n\n1. Traffic management: A service mesh provides advanced traffic control features such as load balancing, circuit breaking, retries, and timeouts.\n\n2. Security: The service mesh can enable secure communication between services using mutual TLS (mTLS) for authentication and encryption.\n\n3. Observability: The service mesh collects telemetry data, including metrics, logs, and traces, to provide visibility into the health and performance of the mesh.\n\n4. Decoupling of application and infrastructure concerns: The service mesh architecture separates the application code from the infrastructure concerns, allowing developers to focus on building business logic while the service mesh handles cross-cutting concerns.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 139,
        "worker_latency_ms": 2026.64499997627,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "infrastructure as code tools",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/data-warehouse-modeling.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/infrastructure-as-code.md"
        ],
        "worker_answer": "The context mentions Terraform as an Infrastructure as Code (IaC) tool. It also briefly references other IaC tools like CloudFormation and Ansible in the security checklist but does not provide details about them.",
        "reference_answer": "Terraform is an open-source Infrastructure as Code (IaC) tool that allows you to define and provision your infrastructure in a declarative manner. It supports a wide range of cloud providers and on-premises resources. Terraform maintains the state of your infrastructure in a state file, which is typically stored in a backend, such as a local file, an S3 bucket, or a HashiCorp Consul backend. Terraform supports state locking to prevent multiple users or processes from modifying the state file at the same time, and state importing to add existing resources to the Terraform state.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 253,
        "worker_latency_ms": 3762.3968329280615,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "monitoring vs observability",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/monitoring-observability.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/design-patterns-behavioral.md",
          "tests/fixtures/scale_docs/service-mesh.md",
          "tests/fixtures/scale_docs/kubernetes-architecture.md"
        ],
        "worker_answer": "Monitoring involves tracking specific metrics and health indicators to detect issues in software systems. Observability goes beyond monitoring by enabling a deeper understanding of system behavior, allowing for the identification and diagnosis of issues through comprehensive data analysis.",
        "reference_answer": "Monitoring and observability are related but distinct concepts. Monitoring focuses on collecting and analyzing metrics, such as the RED (Rate, Errors, Duration) and USE (Utilization, Saturation, Errors) metrics, to track the health and performance of a system. Observability, on the other hand, is a broader concept that encompasses monitoring, as well as other techniques like distributed tracing and log aggregation, to provide a more comprehensive understanding of a system's behavior and enable faster issue resolution.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 292,
        "worker_latency_ms": 3711.7016250267625,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "auto scaling in the cloud",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cost-optimization.md",
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/migration-strategies.md",
          "tests/fixtures/scale_docs/migration-strategies.md",
          "tests/fixtures/scale_docs/well-architected-framework.md"
        ],
        "worker_answer": "Auto-scaling in the cloud dynamically adjusts resources like EC2 instances based on metrics such as CPU utilization. It helps match supply and demand, uses Elastic Load Balancing to distribute traffic, and can involve serverless services like AWS Lambda for event-driven architectures.",
        "reference_answer": "Auto-scaling in the cloud refers to the ability of a system to automatically adjust its resource capacity based on actual load. There are several strategies for implementing auto-scaling, including:\n\n1. Target Tracking Scaling: This adjusts the capacity to maintain a target value for a specific metric, such as average CPU utilization, keeping the target metric as close to the specified value as possible.\n\n2. Step Scaling: This adjusts the capacity based on a set of scaling adjustments, known as step adjustments, which specify a metric value range and the corresponding adjustment to make to the current capacity.\n\n3. Predictive Scaling: This uses load forecasting to automatically scale the capacity ahead of predicted traffic changes, allowing the system to proactively scale resources to meet anticipated demand.",
        "score": {
          "correctness": 2,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.7143
        },
        "tokens_in_context": 240,
        "worker_latency_ms": 4187.8981669433415,
        "judge_raw_response": "{\"correctness\": 2, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "disaster recovery planning",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/database-replication.md",
          "tests/fixtures/scale_docs/disaster-recovery.md",
          "tests/fixtures/scale_docs/database-replication.md",
          "tests/fixtures/scale_docs/disaster-recovery.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md"
        ],
        "worker_answer": "Disaster recovery (DR) planning involves recovering IT systems, data, and operations after a disaster or major outage. Key strategies include backup and restore methods which involve creating backups of critical data, applications, and system configurations to enable recovery from outages or disasters. Effective DR planning also includes developing an incident response plan, implementing a comprehensive backup strategy, and ensuring the availability of key infrastructure components through redundancy.",
        "reference_answer": "Disaster recovery planning involves strategies to recover IT systems, data, and operations in the event of a disaster or major outage. The two key metrics are Recovery Point Objective (RPO), the maximum amount of data that can be lost, and Recovery Time Objective (RTO), the maximum time before normal operations must resume. A fundamental strategy is backup and restore, which involves regularly creating full or incremental backups of critical data and systems that can be used to restore operations. Backups can be stored on-site, off-site, or in the cloud, and the specific backup strategy should be designed to meet the organization's RPO and RTO requirements.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 326,
        "worker_latency_ms": 5709.4236250268295,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "api gateway patterns",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/api-gateway-patterns.md",
          "tests/fixtures/scale_docs/api-gateway-patterns.md",
          "tests/fixtures/scale_docs/api-gateway-patterns.md",
          "tests/fixtures/scale_docs/api-gateway-patterns.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md"
        ],
        "worker_answer": "The context mentions two common API gateway design patterns:\n\n1. **Proxy Pattern**: The simplest implementation where the gateway forwards requests directly to microservices without additional processing.\n2. **Aggregator Pattern**: Used when a client needs data from multiple microservices; the gateway sends requests to relevant services, aggregates responses, and returns a combined result.",
        "reference_answer": "The API gateway design patterns described in the document include:\n\n1. Proxy Pattern: The API gateway forwards requests directly to the appropriate microservice without any additional processing. This is suitable for simple use cases.\n\n2. Aggregator Pattern: The API gateway sends individual requests to relevant services, aggregates the responses, and returns the combined result to the client. This is used when a client needs to retrieve data from multiple microservices.\n\n3. Translator Pattern: The API gateway is responsible for translating the client's request into the format expected by the microservice, and then translating the response back into the client's expected format. This is used when the client and microservice APIs have different data formats or protocols.\n\n4. Decomposer Pattern: The API gateway decomposes the client's request into individual service calls, executes them concurrently, and then composes the final response. This is used when a client request requires data from multiple microservices, but the client does not want to be responsible for orchestrating those requests.",
        "score": {
          "correctness": 2,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.7143
        },
        "tokens_in_context": 391,
        "worker_latency_ms": 5559.328792034648,
        "judge_raw_response": "{\"correctness\": 2, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "cloud storage options",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cloud-storage-options.md",
          "tests/fixtures/scale_docs/cloud-storage-options.md",
          "tests/fixtures/scale_docs/cloud-storage-options.md",
          "tests/fixtures/scale_docs/migration-strategies.md",
          "tests/fixtures/scale_docs/migration-strategies.md"
        ],
        "worker_answer": "The context mentions three main types of cloud storage options: Object, Block, and File.",
        "reference_answer": "Amazon S3 is an object storage service that offers several storage classes for different data access patterns and durability requirements. S3 Standard is for frequently accessed data, S3 Intelligent-Tiering automatically moves data between access tiers, S3 Standard-IA and S3 One Zone-IA are for infrequently accessed data, and S3 Glacier and S3 Glacier Deep Archive are for long-term archival of rarely accessed data. S3 Lifecycle policies can be used to automatically transition objects between storage classes or delete objects based on age. S3 supports various access methods like GET/HEAD/PUT/DELETE, multi-part uploads, batch operations, and data querying with S3 Select and S3 Glacier Select.",
        "score": {
          "correctness": 1,
          "completeness": 2,
          "hallucination": 0,
          "combined": 0.4286
        },
        "tokens_in_context": 60,
        "worker_latency_ms": 1584.6587910782546,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 2, \"hallucination\": 0}"
      },
      {
        "competitor": "langchain",
        "query": "data serialization formats",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/data-lake-architecture.md",
          "tests/fixtures/scale_docs/data-serialization.md"
        ],
        "worker_answer": "JSON, Protobuf, Avro, Parquet, and ORC are the data serialization formats being compared in this context.",
        "reference_answer": "Data serialization formats refer to the various ways of converting data structures or objects into a format that can be stored or transmitted, and then reconstructed later. Some popular data serialization formats include:\n\nJSON (JavaScript Object Notation): A lightweight, human-readable data interchange format that represents data as a collection of name/value pairs and ordered lists of values. It has a simple syntax, is language-independent, and is widely adopted, but can be verbose and has limited data types.\n\nProtobuf (Protocol Buffers): A binary serialization format developed by Google that is compact, fast, and language-neutral. It uses a schema definition language to define the structure of the data, enabling efficient schema evolution.\n\nAvro: A row-oriented data serialization system developed by Apache that provides rich data structures, a compact binary format, and dynamic typing. It supports schema evolution and is often used in big data and stream processing applications.\n\nParquet and ORC: Column-oriented file formats designed for efficient storage and querying of large datasets. They use compression and encoding techniques to achieve high performance and are commonly used in big data ecosystems like Hadoop and Spark.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 355,
        "worker_latency_ms": 3520.6484999507666,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "message queue patterns",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/error-handling-patterns.md",
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/chaos-engineering.md",
          "tests/fixtures/scale_docs/error-handling-patterns.md",
          "tests/fixtures/scale_docs/cloud-design-patterns.md"
        ],
        "worker_answer": "The context describes two communication patterns:\n\n1. **Synchronous (REST/gRPC)**: Direct request-response interaction between services, such as `User Service --HTTP--> Order Service`. This pattern is simple and provides immediate responses but can lead to tight coupling between services and cascading failures.\n\n2. **Asynchronous (Message Queue)**: Indirect interaction where one service sends a message to another, such as `Order Service --Message--> Payment Service`. This pattern offers loose coupling and resilience but may suffer from eventual consistency issues and increased complexity.\n\nFor the question \"message queue patterns,\" the provided context specifically discusses the asynchronous pattern using message queues, highlighting its pros and cons.",
        "reference_answer": "Message queue patterns are a key component of event-driven architecture. The document describes the following relevant patterns:\n\n1. Event Producers: Components that generate events based on actions or state changes.\n2. Event Consumers: Components that subscribe to and process events.\n3. Event Bus: The middleware that facilitates the publishing and subscribing of events.\n\nThese patterns allow for asynchronous, decoupled communication between components in an event-driven system, promoting scalability, fault tolerance, and flexibility.",
        "score": {
          "correctness": 2,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.7143
        },
        "tokens_in_context": 402,
        "worker_latency_ms": 9319.901417009532,
        "judge_raw_response": "{\"correctness\": 2, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "functional programming concepts",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/functional-programming.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/functional-programming.md",
          "tests/fixtures/scale_docs/systemd-services.md",
          "tests/fixtures/scale_docs/functional-programming.md"
        ],
        "worker_answer": "Functional programming concepts include immutability, pure functions, monads, functors, pattern matching, and algebraic data types. These concepts help in building robust, maintainable, and scalable software by creating modular, composable, and testable code that is easier to reason about and less prone to bugs.",
        "reference_answer": "Functional programming concepts include:\n\n1. Immutability: In functional programming, values are immutable, meaning they cannot be changed once created. Instead, new data structures are created with the desired changes.\n\n2. Pure functions: Pure functions are functions that, given the same input, will always return the same output and have no side effects. They do not depend on or modify any external state.\n\nThe source text provides details on the benefits of immutability, such as referential transparency, concurrency, and easier debugging and testing. It also explains the benefits of pure functions, including referential transparency, testability, parallelism, and caching/memoization.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 234,
        "worker_latency_ms": 4553.567999973893,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "linux memory management",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/linux-memory-management.md",
          "tests/fixtures/scale_docs/linux-memory-management.md",
          "tests/fixtures/scale_docs/linux-memory-management.md",
          "tests/fixtures/scale_docs/linux-memory-management.md",
          "tests/fixtures/scale_docs/linux-memory-management.md"
        ],
        "worker_answer": "Linux memory management involves several interrelated components such as virtual memory, page tables, swap space, the OOM killer, huge pages, and NUMA (Non-Uniform Memory Access) awareness to efficiently utilize available system memory.",
        "reference_answer": "Linux memory management involves the use of virtual memory, which allows processes to access more memory than is physically available on the system. This is implemented through page tables, which map virtual memory pages to physical memory frames. The processor's Memory Management Unit (MMU) uses these page tables to translate virtual addresses to physical addresses during memory accesses. Page tables are organized in a multi-level hierarchy, with a top-level page directory and lower-level page tables. When a process attempts to access a virtual address, the MMU uses the page tables to translate the virtual address to a physical address. If the page is not present in the page tables, the MMU generates a page fault, which is handled by the kernel.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 357,
        "worker_latency_ms": 4328.734416980296,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "dns resolution process",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/dns-resolution.md",
          "tests/fixtures/scale_docs/dns-resolution.md",
          "tests/fixtures/scale_docs/dns-resolution.md",
          "tests/fixtures/scale_docs/dns-resolution.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md"
        ],
        "worker_answer": "The DNS resolution process starts with a client checking its local cache for the IP address associated with a domain name. If not found, it queries a recursive resolver which then starts by querying the root DNS servers for information about the TLD server that can handle the next step of the resolution process.",
        "reference_answer": "The DNS resolution process involves the following steps:\n\n1. The client (e.g., web browser) first checks its local cache for the requested domain's IP address. If not found, it sends a recursive query to a DNS resolver.\n\n2. The resolver queries the root DNS servers to find the appropriate top-level domain (TLD) server, then queries the TLD server to locate the authoritative DNS server for the domain.\n\n3. The authoritative server provides the requested information, such as the IP address, which the resolver then caches and returns to the client.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 395,
        "worker_latency_ms": 5373.754750005901,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "connection pooling benefits",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/connection-pooling.md",
          "tests/fixtures/scale_docs/connection-pooling.md",
          "tests/fixtures/scale_docs/connection-pooling.md",
          "tests/fixtures/scale_docs/connection-pooling.md",
          "tests/fixtures/scale_docs/connection-pooling.md"
        ],
        "worker_answer": "Database connection pooling improves performance and scalability by reusing database connections, thus avoiding the overhead of creating new connections for every operation.",
        "reference_answer": "Database connection pooling provides several benefits:\n\n1. Improved performance: By reusing existing database connections instead of creating new ones for every operation, connection pooling reduces the overhead and latency associated with establishing new connections.\n\n2. Increased scalability: Connection pooling allows an application to handle more concurrent requests by efficiently managing a limited number of database connections.\n\n3. Efficient resource utilization: Connection pooling optimizes the use of database server resources by maintaining a pool of reusable connections, reducing the need to create and destroy connections frequently.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 447,
        "worker_latency_ms": 3744.0655000973493,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "cryptography basics",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cryptography-basics.md",
          "tests/fixtures/scale_docs/cryptography-basics.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/cryptography-basics.md"
        ],
        "worker_answer": "Cryptography basics include symmetric encryption, which uses a single shared secret key for both encryption and decryption; and asymmetric encryption (public-key cryptography), which uses a pair of keys, one public for encryption and one private for decryption. Hashing is another fundamental concept that transforms input data into a fixed-size output, used for ensuring data integrity and security.",
        "reference_answer": "Cryptography is the practice of securing information through the use of codes and ciphers. The fundamental concepts of cryptography include:\n\n- Symmetric encryption: A single shared secret key is used to encrypt and decrypt data, such as the Advanced Encryption Standard (AES) algorithm. AES supports 128, 192, and 256-bit key sizes and operates on 128-bit blocks of data.\n- Asymmetric encryption: Two separate keys are used - a public key for encryption and a private key for decryption, such as RSA and ECDSA.\n- Hashing: A one-way mathematical function that converts data into a fixed-size output, such as SHA-256 and bcrypt.\n- Key derivation functions (KDFs): Algorithms that derive secure cryptographic keys from a password or other input, such as PBKDF2 and Argon2.\n\nThese fundamental cryptographic concepts are widely used to secure data, communications, and user authentication.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 403,
        "worker_latency_ms": 5901.2113329954445,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "distributed transactions",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/monitoring-observability.md",
          "tests/fixtures/scale_docs/chaos-engineering.md"
        ],
        "worker_answer": "Distributed transaction strategies are exemplified by the Saga Pattern, where a distributed transaction spans multiple services. Each service performs its part of the transaction, and if any step fails, compensating transactions are used to rollback previous successful steps.",
        "reference_answer": "Distributed transactions refer to the coordination of multiple operations across different systems or databases as a single logical transaction. The source text does not directly discuss distributed transactions, as it focuses on concurrency patterns and constructs within a single system, such as thread pools. The text provides an example implementation of a basic thread pool in Python, but does not mention how to handle distributed transactions or the challenges involved in coordinating multiple systems. The source text does not contain information to answer the query about distributed transactions.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 405,
        "worker_latency_ms": 4974.010415957309,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "container security hardening",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/network-security-hardening.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/container-security.md"
        ],
        "worker_answer": "The context provides a checklist for network security hardening focused on container security, which includes scanning container images for vulnerabilities, implementing secure image building practices, configuring runtime security policies, and ensuring secure configuration of container orchestration platforms. To harden container security according to this checklist:\n\n1. Scan container images for known vulnerabilities and security issues.\n2. Apply best practices in building container images, such as using a secure base image and removing unnecessary components.\n3. Set up runtime security policies to restrict container capabilities.\n4. Securely configure container orchestration platforms.\n\nThese steps aim at enhancing the security posture of containers and their deployment environments.",
        "reference_answer": "Container security hardening involves several key practices:\n\n1. Image scanning: Scanning container images for known vulnerabilities using tools like Trivy, Clair, and Anchore, and scanning for malware using tools like ClamAV.\n2. Runtime protection: Implementing security measures like seccomp and AppArmor to restrict the capabilities of running containers and prevent unauthorized access or actions.\n3. Rootless containers: Using rootless containers, which run as non-root users, to reduce the risk of privilege escalation attacks.\n4. Supply chain security: Ensuring the security of the container image supply chain by verifying the provenance and integrity of container images.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 406,
        "worker_latency_ms": 8702.502290951088,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "well architected framework",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/migration-strategies.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/well-architected-framework.md"
        ],
        "worker_answer": "I don't know. The provided context does not give a direct definition or explanation of the \"well architected framework.\" It only mentions design principles related to Performance Efficiency and Cost Optimization within the AWS Well-Architected Framework.",
        "reference_answer": "The AWS Well-Architected Framework is a set of best practices and design principles to help cloud architects build secure, high-performing, resilient, and efficient infrastructure. The Operational Excellence pillar focuses on the ability to run and monitor systems to deliver business value and continually improve supporting processes and procedures. Key design principles include automating operational procedures, embedding documentation, making frequent and reversible changes, and anticipating and learning from failures. The framework covers areas such as implementing strong change management, maintaining observability, having effective incident response, and continuously improving operations based on data.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 85,
        "worker_latency_ms": 3161.775166983716,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "cost optimization strategies",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/cost-optimization.md",
          "tests/fixtures/scale_docs/cost-optimization.md"
        ],
        "worker_answer": "Cost optimization strategies include right-sizing, reserved instances, spot instances, auto-scaling, and storage tiering.",
        "reference_answer": "Cloud cost optimization strategies include:\n\n1. Right-sizing: Ensuring cloud resources are appropriately sized to meet workload requirements. This involves identifying and resizing oversized resources to better match usage needs.\n\n2. Reserved Instances: Committing to a longer-term usage of cloud resources in exchange for a discounted hourly rate. This can provide significant cost savings compared to on-demand pricing.\n\n3. Spot Instances: Utilizing spare cloud computing capacity at discounted prices. Spot instances can offer substantial cost savings but are subject to interruption.\n\n4. Auto-scaling: Automatically scaling cloud resources up and down based on demand to optimize costs and ensure efficient resource utilization.\n\n5. Storage tiering: Leveraging different storage tiers (e.g., hot, warm, cold) to match data access requirements and minimize storage costs.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 281,
        "worker_latency_ms": 2854.261500062421,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "incident response steps",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/incident-response.md",
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/incident-response.md",
          "tests/fixtures/scale_docs/incident-response.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md"
        ],
        "worker_answer": "The provided context outlines the following incident response procedure for a Severity 1 Incident:\n\n1. Acknowledge the incoming Severity 1 alert.\n2. Determine the affected services and systems based on the alert details.\n3. Engage the primary on-call engineer and the backup on-call engineer.\n4. Initiate a conference call with the incident response team.\n5. Triage the incident, gather relevant logs and metrics, and identify potential root causes.\n6. Implement necessary remediation steps to restore service.",
        "reference_answer": "The key incident response steps outlined in the document are:\n\n1. Detection: Identifying the incident, typically through monitoring systems or user reports.\n2. Triaging: Assessing the incident's severity, impact, and urgency to determine the appropriate response.\n3. Escalation: Notifying the relevant teams or on-call personnel to initiate the incident response process.\n4. Remediation: Investigating, diagnosing, and resolving the incident.\n5. Postmortem: Reviewing the incident, identifying root causes, and documenting lessons learned.\n6. Closure: Formally closing the incident after all necessary actions have been completed.\n\nThe document also describes four incident severity levels, ranging from critical (Severity 1) to low (Severity 4), which determine the response and prioritization of the incident.",
        "score": {
          "correctness": 2,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.7143
        },
        "tokens_in_context": 378,
        "worker_latency_ms": 7274.531207978725,
        "judge_raw_response": "{\"correctness\": 2, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "multi-region deployment",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/multi-region-deployment.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md"
        ],
        "worker_answer": "Deploying applications and services across multiple regions improves availability, reduces latency, and offers disaster recovery. However, it introduces challenges like choosing between active-active or active-passive architectures, data replication methods, and DNS failover mechanisms. The best strategy depends on specific application needs and constraints.",
        "reference_answer": "Multi-region deployment refers to the strategy of deploying an application or service across multiple geographic regions. This can be done in an active-active or active-passive configuration.\n\nIn an active-active setup, the application runs in multiple regions simultaneously, with all regions actively serving user traffic. This provides high availability and low latency, as users can be routed to the nearest region. However, it requires more complex coordination and data replication between regions.\n\nAlternatively, an active-passive configuration has one region actively serving traffic, while the other region(s) are in standby mode, ready to take over in the event of a failover. This is simpler to implement but offers lower availability and potentially higher latency.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 380,
        "worker_latency_ms": 5145.08912502788,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "ssh configuration security",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/ssh-configuration.md",
          "tests/fixtures/scale_docs/ssh-configuration.md",
          "tests/fixtures/scale_docs/ssh-configuration.md",
          "tests/fixtures/scale_docs/ssh-configuration.md",
          "tests/fixtures/scale_docs/ssh-configuration.md"
        ],
        "worker_answer": "SSH configuration can be secured by applying several measures such as using strong encryption, disabling password authentication in favor of public-key authentication, preventing root login directly via SSH, limiting allowed users to connect, and enabling detailed logging for better monitoring. These measures can be implemented by adding specific lines in the `~/.ssh/config` file on Unix-like systems or `%USERPROFILE%\\.ssh\\config.exe` on Windows.",
        "reference_answer": "The SSH configuration file allows you to define custom settings for connecting to remote hosts, including specifying the host, user, identity file, and port. It also supports features like ProxyCommand to connect through a bastion host. SSH authentication relies on public-key cryptography, where users have a public key shared with remote servers and a private key kept secure on their local machine. To manage multiple keys, you can use the IdentityFile option in the SSH config file to specify which key to use for each host. The SSH agent is a background process that holds your private keys in memory, allowing you to authenticate with remote servers without constantly entering your passphrase.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 458,
        "worker_latency_ms": 7004.888124996796,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "vpn tunneling protocols",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md"
        ],
        "worker_answer": "The context mentions several VPN tunneling protocols including IPSec, WireGuard, and OpenVPN.",
        "reference_answer": "VPN tunneling protocols refer to the various methods used to establish secure, encrypted connections between devices or networks over a public network like the internet. The two main VPN protocols discussed in the source text are:\n\nIPSec (Internet Protocol Security): IPSec operates at the network layer and provides encryption, authentication, and data integrity for IP traffic. It has two main modes: Transport Mode, which encrypts the payload but leaves the original IP headers intact, and Tunnel Mode, which encapsulates the entire original IP packet within a new IP packet with IPSec headers. IPSec uses the ESP and AH protocols, as well as the IKE protocol for key exchange.\n\nWireGuard: WireGuard is a newer, open-source VPN protocol that aims to be faster, simpler, and more secure than existing solutions. It operates at the network layer and uses modern cryptographic primitives like Curve25519 and ChaCha20-Poly1305. WireGuard has a minimal attack surface and uses a peer-to-peer architecture, allowing devices to communicate directly without a central VPN server.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 368,
        "worker_latency_ms": 3405.294958036393,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "websocket protocol overview",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/websocket-protocol.md",
          "tests/fixtures/scale_docs/websocket-protocol.md",
          "tests/fixtures/scale_docs/websocket-protocol.md",
          "tests/fixtures/scale_docs/websocket-protocol.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md"
        ],
        "worker_answer": "The WebSocket protocol is a computer communications protocol that provides a two-way communication channel over a single TCP connection. It was designed as an efficient alternative to traditional HTTP-based polling for real-time web applications.",
        "reference_answer": "The WebSocket protocol is a computer communications protocol that provides a two-way communication channel over a single TCP connection. It was designed as an efficient alternative to traditional HTTP-based polling for real-time web applications.\n\nThe WebSocket handshake is the process of establishing a WebSocket connection between a client and a server. The client initiates the handshake with an HTTP request that includes specific headers, and the server responds with a set of headers indicating that it is switching the protocol to WebSocket.\n\nAfter the handshake is complete, the client and server can start exchanging WebSocket frames, which are the basic units of data transmission in the WebSocket protocol. Each frame has a specific structure, including fields for the frame type, payload length, and payload data.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 285,
        "worker_latency_ms": 3933.880958938971,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "helm charts fundamentals",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/helm-charts.md",
          "tests/fixtures/scale_docs/helm-charts.md",
          "tests/fixtures/scale_docs/helm-charts.md",
          "tests/fixtures/scale_docs/helm-charts.md",
          "tests/fixtures/scale_docs/helm-charts.md"
        ],
        "worker_answer": "Helm charts are the unit of packaging for Helm, a Kubernetes package manager. They simplify the deployment and management of complex applications by allowing you to define, install, and upgrade Kubernetes applications through templates, values, dependencies, hooks, testing, and repository management.",
        "reference_answer": "Helm charts are the unit of packaging for the Helm package manager for Kubernetes. The key fundamentals of Helm charts include:\n\nTemplates: Helm templates are written in the Go template language and used to generate Kubernetes manifest files. They can include variables, functions, and control structures for dynamic and reusable configurations.\n\nValues: The `values.yaml` file defines the default values for the chart's templates, which can be overridden when the chart is installed or upgraded.\n\nDependencies: Helm charts can depend on other Helm charts, called \"subchart\" dependencies, which are specified in the `Chart.yaml` file.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 549,
        "worker_latency_ms": 6139.074999955483,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "linux process management",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/linux-process-management.md",
          "tests/fixtures/scale_docs/linux-process-management.md",
          "tests/fixtures/scale_docs/systemd-services.md",
          "tests/fixtures/scale_docs/package-management.md",
          "tests/fixtures/scale_docs/linux-performance-tuning.md"
        ],
        "worker_answer": "Linux process management involves managing processes through various states such as Running, Waiting, Stopped, and Zombie. It includes operations like fork/exec, handling signals, adjusting process priority with nice/renice commands, and using control groups (cgroups) and namespaces for resource management.",
        "reference_answer": "Linux process management involves the creation, execution, and monitoring of processes. The key aspects are:\n\n1. Fork and Exec: The `fork()` system call creates a new process (child) that is a copy of the calling process (parent). The `exec()` system call replaces the current process image with a new process image, effectively terminating the old process and starting a new one.\n\n2. Process States: Processes in Linux can be in one of several states - running, waiting, stopped, or zombie. The `ps` command can be used to view the current state of processes.\n\n3. Signals: Processes can receive and handle various signals, which are software interrupts that notify a process of an event. Signals can be used to control and manage processes.\n\n4. Nice and Renice: The `nice` and `renice` commands allow you to adjust the scheduling priority of a process, which determines how much CPU time it receives.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 437,
        "worker_latency_ms": 5305.339958984405,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "network troubleshooting tools",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/network-troubleshooting.md",
          "tests/fixtures/scale_docs/network-security-hardening.md",
          "tests/fixtures/scale_docs/connection-pooling.md",
          "tests/fixtures/scale_docs/network-troubleshooting.md",
          "tests/fixtures/scale_docs/chaos-engineering.md"
        ],
        "worker_answer": "I don't know. The provided context does not specify any particular network troubleshooting tools, only that there are monitoring tools for connection pool metrics and an example of using the Chaos Toolkit for simulating network latency.",
        "reference_answer": "The key network troubleshooting tools mentioned in the source text are:\n\n1. tcpdump: A powerful network packet analysis tool that allows capturing and inspecting network traffic on a specific interface. It can be used for troubleshooting network issues, analyzing network behavior, and identifying potential security threats.\n\n2. traceroute: A network diagnostic tool that helps identify the path a packet takes from your computer to a destination IP address or hostname. It is useful for troubleshooting connectivity issues and identifying where in the network a problem might be occurring.\n\nThe text provides details on the basic usage and common use cases for each of these tools, including examples of how to use them for tasks like capturing and analyzing network traffic, troubleshooting connectivity issues, identifying security threats, and debugging application-level issues.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 96,
        "worker_latency_ms": 2776.239875005558,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "database replication types",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/database-replication.md",
          "tests/fixtures/scale_docs/database-replication.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md",
          "tests/fixtures/scale_docs/database-replication.md",
          "tests/fixtures/scale_docs/database-replication.md"
        ],
        "worker_answer": "The context mentions several replication architectures and approaches, but specifically details primary-replica (master-slave) replication as the most common type. It also alludes to different strategies based on consistency requirements, including synchronous replication and asynchronous/eventual consistency models. However, the context does not provide an exhaustive list of all database replication types.",
        "reference_answer": "Database replication can take several forms:\n\nPrimary-replica (or master-slave) replication has a single primary database that accepts all writes, and changes are asynchronously replicated to one or more read-only replica databases. This provides high availability, scalability for reads, and disaster recovery.\n\nMulti-primary (or multi-master) replication allows writes on multiple databases, with conflicts resolved automatically or manually. This supports higher write throughput but introduces more complexity around conflict resolution.\n\nSynchronous replication ensures that changes are applied atomically across all replicas, providing stronger consistency guarantees but potentially lower availability during failures.\n\nAsynchronous replication, the more common approach, allows the replicas to lag behind the primary, improving availability but potentially leading to data loss if the primary fails.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 357,
        "worker_latency_ms": 5201.72862499021,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "nosql design patterns",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/nosql-patterns.md",
          "tests/fixtures/scale_docs/nosql-patterns.md",
          "tests/fixtures/scale_docs/nosql-patterns.md",
          "tests/fixtures/scale_docs/nosql-patterns.md",
          "tests/fixtures/scale_docs/nosql-patterns.md"
        ],
        "worker_answer": "NoSQL design patterns include Denormalization, Aggregation, Time-series Data Modeling, Graph Traversal Patterns, and Document Design Principles.",
        "reference_answer": "NoSQL databases employ flexible, schema-less or schema-optional models that can accommodate unstructured, semi-structured, and rapidly changing data. Key NoSQL data modeling patterns include:\n\n1. Denormalization: NoSQL encourages denormalization to optimize for read performance and query patterns, minimizing the need for complex joins.\n2. Aggregation: NoSQL data models are structured to match common query patterns, even if it results in duplicating data across different entities.\n3. Time-series data modeling: NoSQL databases are well-suited for time-series data, allowing efficient storage and retrieval of data points over time.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 376,
        "worker_latency_ms": 3554.449209012091,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "sql query optimization",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/sql-query-optimization.md",
          "tests/fixtures/scale_docs/sql-query-optimization.md",
          "tests/fixtures/scale_docs/sql-query-optimization.md",
          "tests/fixtures/scale_docs/sql-query-optimization.md",
          "tests/fixtures/scale_docs/sql-query-optimization.md"
        ],
        "worker_answer": "SQL query optimization involves techniques such as using EXPLAIN plans, selecting appropriate indexes, rewriting queries, choosing efficient join algorithms, and analyzing database statistics to enhance the performance and scalability of database-driven applications.",
        "reference_answer": "The key techniques for SQL query optimization include:\n\n1. Using EXPLAIN plans to analyze the execution plan and identify potential performance bottlenecks, such as full table scans or suboptimal join algorithms.\n2. Selecting appropriate indexes to speed up data lookups, especially for columns used in the WHERE clause.\n3. Rewriting queries to use more efficient join algorithms, such as hash joins or merge joins, instead of nested loop joins.\n4. Analyzing database statistics to ensure the query optimizer has accurate information about the data distribution and cardinality, which can help it generate better execution plans.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 311,
        "worker_latency_ms": 3761.4556669723243,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "data pipeline design",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-pipeline-design.md",
          "tests/fixtures/scale_docs/data-pipeline-design.md",
          "tests/fixtures/scale_docs/data-pipeline-design.md",
          "tests/fixtures/scale_docs/data-pipeline-design.md",
          "tests/fixtures/scale_docs/data-pipeline-design.md"
        ],
        "worker_answer": "Data pipeline design involves several key considerations including scalability, reliability, and performance. It requires transforming, integrating, and moving data from various sources to a target destination efficiently and effectively while ensuring data quality. Architectural patterns play a crucial role in achieving these goals, though specific patterns are not detailed in the provided context.",
        "reference_answer": "A data pipeline is a series of steps that transform, integrate, and move data from various sources to a target destination. The key components of a data pipeline include data sources, ingestion, transformation, loading, orchestration, and monitoring and alerting. When designing a data pipeline, important considerations include data volume and velocity, data variety, data quality, scalability, reliability, performance, maintainability, and security and governance. The pipeline should be designed to handle the expected data load, accommodate diverse data formats, ensure data integrity, scale as needed, and be resilient, efficient, and easy to maintain.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 271,
        "worker_latency_ms": 4667.593125021085,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "testing strategies for software",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/testing-strategies.md",
          "tests/fixtures/scale_docs/testing-strategies.md",
          "tests/fixtures/scale_docs/testing-strategies.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md"
        ],
        "worker_answer": "Effective software testing involves a multifaceted approach that includes different strategies such as unit testing, integration testing, end-to-end testing, test doubles (like mocks and stubs), property-based testing, and mutation testing. These strategies aim to ensure the quality and reliability of the codebase by covering various aspects of software functionality.",
        "reference_answer": "The primary testing strategies for software include:\n\n1. Unit Testing: Verifying the correctness of individual, isolated components or units of the system.\n2. Integration Testing: Evaluating how different components or modules of the system work together.\n3. End-to-End (E2E) Testing: Validating the entire system, including user interactions and the flow of data through the application.\n\nUnit tests focus on exercising a single, isolated component or functionality, ensuring it behaves as expected. Integration tests examine the interactions and data flow between different components or modules, simulating complete end-to-end workflows. These testing strategies play a crucial role in ensuring the overall quality and stability of the software system.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 332,
        "worker_latency_ms": 5143.077082931995,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "dependency injection benefits",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/dependency-injection.md",
          "tests/fixtures/scale_docs/dependency-injection.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/data-serialization.md"
        ],
        "worker_answer": "- Testability: easier to write unit tests by substituting mock or stub implementations of dependencies.\n- Flexibility: easily swap out implementation, enabling greater flexibility and maintainability.\n- Modularity: promotes a modular design for independent development, testing, and deployment of components.",
        "reference_answer": "Dependency injection provides several key benefits:\n\n1. Testability: It makes it easier to write unit tests by allowing you to easily substitute mock or stub implementations of dependencies.\n2. Flexibility: Decoupling objects from their dependencies enables you to easily swap out implementations, improving flexibility and maintainability.\n3. Modularity: Dependency injection promotes a modular design, as components can be developed, tested, and deployed independently.\n4. Inversion of Control: Dependency injection inverts the control of object creation and lifetime management, allowing a central authority (the IoC container) to manage these concerns.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 229,
        "worker_latency_ms": 4011.020165984519,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "behavioral design patterns",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/design-patterns-behavioral.md",
          "tests/fixtures/scale_docs/design-patterns-creational.md",
          "tests/fixtures/scale_docs/design-patterns-structural.md",
          "tests/fixtures/scale_docs/design-patterns-behavioral.md",
          "tests/fixtures/scale_docs/design-patterns-behavioral.md"
        ],
        "worker_answer": "The behavioral design patterns covered in the provided context are Strategy, Observer, and State.",
        "reference_answer": "Behavioral design patterns are a category of design patterns that focus on the communication between objects and how they interact with each other. Some key behavioral design patterns include:\n\n- Observer: Allows objects to be notified of changes to the state of another object. It provides a way to implement event-driven architectures.\n- Strategy: Defines a family of algorithms, encapsulates each one, and makes them interchangeable. Clients can select the appropriate algorithm at runtime.\n- Command: Encapsulates a request as an object, allowing for the parameterization of clients with different requests, queue or log requests, and support undoable operations.\n\nThese patterns provide flexible ways to manage object interactions and communication, making the code more modular, extensible, and maintainable.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 387,
        "worker_latency_ms": 3069.4891249295324,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "creational design patterns",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/design-patterns-creational.md",
          "tests/fixtures/scale_docs/design-patterns-creational.md",
          "tests/fixtures/scale_docs/design-patterns-creational.md",
          "tests/fixtures/scale_docs/data-pipeline-design.md",
          "tests/fixtures/scale_docs/design-patterns-structural.md"
        ],
        "worker_answer": "The five creational design patterns explored in the provided context are Singleton, Factory Method, Abstract Factory, Builder, and Prototype.",
        "reference_answer": "The creational design patterns are a set of design patterns that deal with object creation mechanisms, aiming to create objects in a manner suitable to the situation. The key creational design patterns are:\n\n- Singleton: Ensures a class has only one instance and provides a global point of access to it.\n- Factory Method: Defines an interface for creating an object, but lets subclasses decide which class to instantiate.\n- Abstract Factory: Provides an interface for creating families of related or dependent objects without specifying their concrete classes.\n- Builder: Separates the construction of a complex object from its representation so that the same construction process can create different representations.\n- Prototype: Specifies the kind of object to create using a prototypical instance, and creates new objects by copying this prototype.\n\nThese patterns provide different ways to create objects, allowing for flexibility, modularity, and testability in software design.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 358,
        "worker_latency_ms": 3247.8514589602128,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "how to bake sourdough in a kubernetes cluster",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/kubernetes-architecture.md",
          "tests/fixtures/scale_docs/kubernetes-architecture.md",
          "tests/fixtures/scale_docs/kubernetes-architecture.md",
          "tests/fixtures/scale_docs/kubernetes-architecture.md",
          "tests/fixtures/scale_docs/kubernetes-architecture.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 324,
        "worker_latency_ms": 2531.4115410437807,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "best brand of hiking boots for Mars",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/database-sharding.md",
          "tests/fixtures/scale_docs/package-management.md",
          "tests/fixtures/scale_docs/package-management.md",
          "tests/fixtures/scale_docs/kubernetes-architecture.md",
          "tests/fixtures/scale_docs/data-lake-architecture.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 427,
        "worker_latency_ms": 4739.298208034597,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "quantum entanglement in photosynthesis",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/http2-http3.md",
          "tests/fixtures/scale_docs/secrets-management.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/concurrency-patterns.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 324,
        "worker_latency_ms": 2658.7049170630053,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "the secret to eternal life according to python docs",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cryptography-basics.md",
          "tests/fixtures/scale_docs/cryptography-basics.md",
          "tests/fixtures/scale_docs/secrets-management.md",
          "tests/fixtures/scale_docs/cryptography-basics.md",
          "tests/fixtures/scale_docs/cryptography-basics.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 490,
        "worker_latency_ms": 3990.742457914166,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "where did I leave my keys",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/database-migrations.md",
          "tests/fixtures/scale_docs/ssh-configuration.md",
          "tests/fixtures/scale_docs/incident-response.md",
          "tests/fixtures/scale_docs/ssh-configuration.md",
          "tests/fixtures/scale_docs/tls-ssl.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 366,
        "worker_latency_ms": 3330.650124931708,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "recipe for disaster in distributed systems",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md",
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 195,
        "worker_latency_ms": 1684.2237079981714,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "blue whales vs sperm whales",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/database-replication.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/data-serialization.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 392,
        "worker_latency_ms": 2709.2471659416333,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "history of the Roman Empire",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-warehouse-modeling.md",
          "tests/fixtures/scale_docs/infrastructure-as-code.md",
          "tests/fixtures/scale_docs/linux-networking-config.md",
          "tests/fixtures/scale_docs/linux-memory-management.md",
          "tests/fixtures/scale_docs/linux-performance-tuning.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 1,
          "combined": 0.4286
        },
        "tokens_in_context": 227,
        "worker_latency_ms": 1903.2722909469157,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "how to play the banjo",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/configuration-management.md",
          "tests/fixtures/scale_docs/spark-fundamentals.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/functional-programming.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 227,
        "worker_latency_ms": 2288.046499947086,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "why is the sky blue",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/database-migrations.md",
          "tests/fixtures/scale_docs/ci-cd-pipelines.md",
          "tests/fixtures/scale_docs/design-patterns-behavioral.md",
          "tests/fixtures/scale_docs/data-warehouse-modeling.md",
          "tests/fixtures/scale_docs/ci-cd-pipelines.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 382,
        "worker_latency_ms": 2697.228999924846,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "symptoms of a cold",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/disaster-recovery.md"
        ],
        "worker_answer": "I don't know. The provided context does not describe the symptoms of a cold start, only what causes them and some strategies to mitigate their impact.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 370,
        "worker_latency_ms": 3750.882791937329,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "movie recommendations for tonight",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/infrastructure-as-code.md",
          "tests/fixtures/scale_docs/linux-networking-config.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/scale_docs/package-management.md",
          "tests/fixtures/scale_docs/api-gateway-patterns.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 33,
        "worker_latency_ms": 909.0613749576733,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "stock market predictions for 2026",
        "category": "adversarial",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-lake-architecture.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/auto-scaling.md",
          "tests/fixtures/scale_docs/concurrency-patterns.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 314,
        "worker_latency_ms": 2675.3627080470324,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "philosophy of mind and AI",
        "category": "adversarial",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/migration-strategies.md",
          "tests/fixtures/scale_docs/concurrency-patterns.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 67,
        "worker_latency_ms": 1145.9769160719588,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "meaning of life 42",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-lake-architecture.md",
          "tests/fixtures/scale_docs/data-lake-architecture.md",
          "tests/fixtures/scale_docs/incident-response.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/infrastructure-as-code.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 26,
        "worker_latency_ms": 913.6784169822931,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "how to win friends and influence people",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/nosql-patterns.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/nosql-patterns.md",
          "tests/fixtures/scale_docs/serverless-architecture.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 281,
        "worker_latency_ms": 2481.074500014074,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "yoga for beginners",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/stream-processing.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/linux-networking-config.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 200,
        "worker_latency_ms": 1726.2980840168893,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "langchain",
        "query": "best coffee shops in London",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/nosql-patterns.md",
          "tests/fixtures/scale_docs/incident-response.md",
          "tests/fixtures/scale_docs/design-patterns-structural.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/data-serialization.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 263,
        "worker_latency_ms": 2509.582708007656,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "gardening tips for summer",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/design-patterns-behavioral.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 179,
        "worker_latency_ms": 1704.1718330001459,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "how to repair a flat tire",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/incident-response.md",
          "tests/fixtures/scale_docs/database-backup-recovery.md",
          "tests/fixtures/scale_docs/database-backup-recovery.md",
          "tests/fixtures/scale_docs/incident-response.md",
          "tests/fixtures/scale_docs/disaster-recovery.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 400,
        "worker_latency_ms": 2744.97912498191,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "history of the internet",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/http2-http3.md",
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md",
          "tests/fixtures/scale_docs/infrastructure-as-code.md",
          "tests/fixtures/scale_docs/linux-networking-config.md",
          "tests/fixtures/scale_docs/http2-http3.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 255,
        "worker_latency_ms": 1926.3837500475347,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "space travel in the future",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/concurrency-patterns.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/database-migrations.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/websocket-protocol.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 329,
        "worker_latency_ms": 2932.015583035536,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "the art of war summary",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/secrets-management.md",
          "tests/fixtures/scale_docs/secrets-management.md",
          "tests/fixtures/scale_docs/disaster-recovery.md",
          "tests/fixtures/scale_docs/disaster-recovery.md",
          "tests/fixtures/scale_docs/infrastructure-as-code.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 44,
        "worker_latency_ms": 952.4820420192555,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "langchain",
        "query": "meditation techniques",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/stream-processing.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/chaos-engineering.md",
          "tests/fixtures/scale_docs/stream-processing.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 287,
        "worker_latency_ms": 2281.3766660401598,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "learning a new language fast",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-warehouse-modeling.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/data-quality.md",
          "tests/fixtures/scale_docs/serverless-architecture.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 146,
        "worker_latency_ms": 1501.9168748985976,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "climate change impacts",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/chaos-engineering.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/scale_docs/incident-response.md",
          "tests/fixtures/scale_docs/compliance-frameworks.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 300,
        "worker_latency_ms": 2091.549750068225,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "renewable energy sources",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/scale_docs/disaster-recovery.md",
          "tests/fixtures/scale_docs/disaster-recovery.md",
          "tests/fixtures/scale_docs/disaster-recovery.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 368,
        "worker_latency_ms": 2484.2329589882866,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "healthy eating habits",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/cloud-design-patterns.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 108,
        "worker_latency_ms": 920.7742080325261,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "travel tips for Japan",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/api-gateway-patterns.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/design-patterns-behavioral.md",
          "tests/fixtures/scale_docs/websocket-protocol.md",
          "tests/fixtures/scale_docs/sql-query-optimization.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 412,
        "worker_latency_ms": 2715.4706669971347,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "langchain",
        "query": "basics of photography",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/container-fundamentals.md",
          "tests/fixtures/scale_docs/penetration-testing-methodology.md",
          "tests/fixtures/scale_docs/container-fundamentals.md",
          "tests/fixtures/scale_docs/infrastructure-as-code.md",
          "tests/fixtures/scale_docs/linux-networking-config.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 147,
        "worker_latency_ms": 1495.358790969476,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      }
    ],
    "candlekeep-simple": [
      {
        "competitor": "candlekeep-simple",
        "query": "What is semantic search?",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/semantic-search.md",
          "tests/fixtures/sample_docs/vector-databases.md"
        ],
        "worker_answer": "Semantic search goes beyond keyword matching to understand the meaning and intent behind queries.",
        "reference_answer": "Semantic search is a method of information retrieval that goes beyond simple keyword matching to understand the meaning and intent behind queries. It involves techniques like query preprocessing to handle negation and expand queries with synonyms, as well as ranking and reranking approaches that consider metadata matches, recency, popularity, and other relevance signals. The goal of semantic search is to improve the accuracy and relevance of search results.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 861,
        "worker_latency_ms": 5202.448833035305,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "vector database",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/vector-databases.md"
        ],
        "worker_answer": "Vector databases store and retrieve data based on semantic similarity using embeddings, which are numerical representations of text that capture semantic meaning. They perform similarity search by calculating distances between vectors in high-dimensional space, enabling applications like semantic search engines, recommendation systems, question answering systems, and document clustering/classification. Popular vector databases include ChromaDB, Pinecone, Weaviate, and Milvus.",
        "reference_answer": "A vector database is a type of database that stores and retrieves data based on semantic similarity rather than exact keyword matches. Vector databases use embeddings, which are numerical representations of text that capture semantic meaning, to represent documents as high-dimensional vectors. This allows the database to perform similarity search, where it finds documents with similar meanings by calculating distances between vectors in high-dimensional space. Common use cases for vector databases include semantic search engines, recommendation systems, question answering systems, and document clustering and classification.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 391,
        "worker_latency_ms": 6062.260042061098,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "REST API design",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/api-design.md",
          "tests/fixtures/sample_docs/database-design.md",
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/api-gateway-patterns.md",
          "tests/fixtures/scale_docs/api-security.md"
        ],
        "worker_answer": "REST API design follows the principles of statelessness, resource-based URLs, standard HTTP methods and status codes. It recommends using nouns for resources in URL structure, including versioning in the URL or headers, and returning consistent error formats. Pagination is handled through query parameters like `page` and `limit`, with metadata included in the response.",
        "reference_answer": "REST API design follows several key principles:\n\n- Stateless: Each request contains all the information needed to process it, without relying on server-side state.\n- Resource-based: URLs represent resources (nouns) rather than actions (verbs).\n- HTTP methods: Standard HTTP methods like GET, POST, PUT, and DELETE are used to perform CRUD operations on resources.\n- Standard status codes: APIs return consistent HTTP status codes like 200 OK, 404 Not Found, and 500 Server Error.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 3622,
        "worker_latency_ms": 21043.83520805277,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "database normalization",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/database-design.md"
        ],
        "worker_answer": "Normalization in the context provided refers to the process of organizing data to reduce redundancy and improve integrity through a series of normal forms:\n\n1. **First Normal Form (1NF)**: Ensures atomic values in columns, unique column names, and that order does not matter.\n2. **Second Normal Form (2NF)**: Requires the database to be in 1NF and ensures no partial dependencies, meaning all non-key columns must depend on the entire primary key.\n3. **Third Normal Form (3NF)**: Requires the database to be in 2NF and ensures no transitive dependencies, meaning non-key columns should not depend on other non-key columns.",
        "reference_answer": "Database normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. The key principles of database normalization are:\n\n1. First Normal Form (1NF): Ensure that all columns contain atomic values (no arrays or lists) and each column has a unique name.\n2. Second Normal Form (2NF): Ensure that all non-key columns are fully dependent on the primary key, with no partial dependencies.\n3. Third Normal Form (3NF): Ensure that non-key columns do not have transitive dependencies, meaning they do not depend on other non-key columns.\n\nAdhering to these normalization principles helps to create a well-structured, efficient, and maintainable database schema.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 507,
        "worker_latency_ms": 9557.204624987207,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "HTTP/2 vs HTTP/3 performance",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/http2-http3.md",
          "tests/fixtures/sample_docs/api-design.md"
        ],
        "worker_answer": "HTTP/3 aims to further improve upon the performance of HTTP/2 by using QUIC, which offers features like faster connection establishment due to UDP, improved congestion control, and reduced latency. HTTP/3 retains the multiplexing capabilities of HTTP/2 but operates over QUIC, enhancing reliability and stability.",
        "reference_answer": "HTTP/2 offers several performance improvements over HTTP/1.1, including:\n\nMultiplexing: HTTP/2 allows multiple requests and responses to be in flight simultaneously over a single TCP connection, reducing latency compared to the serial request-response model of HTTP/1.1.\n\nServer Push: HTTP/2 enables the server to proactively send resources to the client before they are requested, further improving performance by reducing round trips.\n\nHeader Compression: HTTP/2 compresses request and response headers, reducing the amount of data that needs to be transmitted.\n\nIn contrast, HTTP/3 is built on top of the QUIC protocol, which uses UDP instead of TCP. This allows for faster connection establishment and recovery from packet loss, potentially improving performance in certain network conditions compared to HTTP/2.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 4647,
        "worker_latency_ms": 21361.43345804885,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "PostgreSQL 15 internals",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/postgresql-internals.md"
        ],
        "worker_answer": "The provided context does not contain specific information about PostgreSQL 15 internals. It covers general concepts like MVCC, WAL, vacuum, query planner, indexes, and partitioning in PostgreSQL but does not specify details about version 15. Therefore, the answer is:\n\nI don't know.",
        "reference_answer": "PostgreSQL 15 uses a Multiversion Concurrency Control (MVCC) system to manage concurrent transactions and ensure data consistency. MVCC allows multiple transactions to access the same data simultaneously without causing conflicts or data corruption. PostgreSQL also uses a Write-Ahead Logging (WAL) system to ensure data durability and consistency, where changes are first recorded in the WAL before being applied to the database.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 3978,
        "worker_latency_ms": 21176.51704198215,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "Apache Kafka 3.0 features",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/apache-kafka.md",
          "tests/fixtures/scale_docs/data-lake-architecture.md",
          "tests/fixtures/scale_docs/data-pipeline-design.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about Apache Kafka 3.0 features.",
        "reference_answer": "Apache Kafka 3.0 introduced several new features:\n\n1. Improved Kafka Streams API: Kafka Streams 3.0 includes enhancements to the Kafka Streams API, such as improved support for stateful processing and new operators for data transformation and aggregation.\n\n2. Enhanced Kafka Connect: Kafka Connect 3.0 includes improvements to the Kafka Connect framework, including better support for data transformation and improved error handling.\n\n3. Expanded Kafka Security: Kafka 3.0 includes expanded security features, such as support for SASL/SCRAM authentication and improved integration with external authentication providers.\n\n4. Improved Kafka Monitoring: Kafka 3.0 includes enhancements to the Kafka monitoring and metrics system, including new metrics and improved visualization tools.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 1,
          "combined": 0.4286
        },
        "tokens_in_context": 6543,
        "worker_latency_ms": 17167.97324991785,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "TLS 1.3 handshake process",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/tls-ssl.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/scale_docs/websocket-protocol.md",
          "tests/fixtures/scale_docs/service-mesh.md",
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md"
        ],
        "worker_answer": "I don't know.\n\nThe provided context does not contain information about the TLS 1.3 handshake process specifically. It details the TLS/SSL handshake up to version 1.2 and other related topics but does not cover TLS 1.3 explicitly.",
        "reference_answer": "The TLS 1.3 handshake process involves the following key steps:\n\n1. Client Hello: The client sends a message to the server that includes the TLS version, a list of supported cipher suites, compression methods, and a random value.\n\n2. Server Hello: The server responds with a message that includes the selected TLS version, cipher suite, compression method, and a random value.\n\n3. Server Certificate: The server sends its X.509 certificate chain to the client, including the server's public key certificate, any intermediate CA certificates, and the root CA certificate.\n\n4. Client Key Exchange: The client generates a pre-master secret and encrypts it with the server's public key from the certificate, then sends this encrypted pre-master secret to the server.\n\n5. Change Cipher Spec: Both the client and server send messages to indicate that all future communications will be encrypted using the negotiated cipher suite and keys.\n\n6. Finished: The client and server exchange encrypted \"Finished\" messages to confirm the successful completion of the handshake.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 6114,
        "worker_latency_ms": 21690.154667012393,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "Kubernetes v1.24 removal of dockershim",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/kubernetes-architecture.md",
          "tests/fixtures/scale_docs/container-security.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "Kubernetes v1.24 will remove the dockershim, which was a component that provided compatibility between Kubernetes and the Docker container runtime. With this removal, Kubernetes will no longer support the Docker runtime directly, and users will need to use alternative container runtimes like containerd or CRI-O. This change is part of Kubernetes' effort to decouple the container runtime from the core Kubernetes components, allowing for more flexibility and innovation in the container runtime ecosystem.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 3869,
        "worker_latency_ms": 16328.981708036736,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "gRPC over HTTP/2",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/http2-http3.md",
          "tests/fixtures/scale_docs/grpc-protocol.md",
          "tests/fixtures/scale_docs/grpc-protocol.md",
          "tests/fixtures/scale_docs/grpc-protocol.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about gRPC over HTTP/2.",
        "reference_answer": "gRPC is a modern open-source remote procedure call (RPC) framework that uses HTTP/2 as the underlying transport protocol. The key features of gRPC over HTTP/2 are:\n\n1. Efficient binary serialization using Protocol Buffers (protobuf) for data exchange.\n2. Bidirectional streaming support, allowing for efficient real-time communication between clients and servers.\n3. Automatic generation of client and server stubs from protobuf service definitions, simplifying application development.\n4. Support for deadlines and cancellation, enabling robust and responsive distributed systems.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 7246,
        "worker_latency_ms": 1621.5509169269353,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "Redis 7.0 Multi-part AOF",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/redis-data-structures.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/helm-charts.md",
          "tests/fixtures/scale_docs/disaster-recovery.md",
          "tests/fixtures/scale_docs/well-architected-framework.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "Redis 7.0 introduced a new feature called \"Multi-part AOF\", which enhances the Append-Only File (AOF) persistence model. The Multi-part AOF allows the AOF file to be split into multiple files, improving the performance and reliability of the AOF persistence. This feature enables more efficient handling of large AOF files, as the Redis server can load and rewrite the AOF in a more scalable manner.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 3150,
        "worker_latency_ms": 18113.37120900862,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "Elasticsearch 8.x security changes",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/api-security.md",
          "tests/fixtures/scale_docs/elasticsearch-basics.md",
          "tests/fixtures/scale_docs/linux-logging.md",
          "tests/fixtures/scale_docs/owasp-top-10.md",
          "tests/fixtures/scale_docs/secrets-management.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about Elasticsearch 8.x security changes.",
        "reference_answer": "Elasticsearch 8.x introduced several security changes:\n\n1. Mandatory TLS encryption: Elasticsearch 8.x requires TLS encryption for all HTTP and transport connections by default. This enhances the security of data transmission.\n\n2. Removal of basic authentication: Elasticsearch 8.x no longer supports basic authentication. Users must now authenticate using token-based or PKI-based authentication methods.\n\n3. Deprecation of the `elasticsearch-users` file: The `elasticsearch-users` file for managing user credentials has been deprecated in favor of the new Elasticsearch security plugin.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 3511,
        "worker_latency_ms": 19074.543707887642,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "OWASP Top 10 2021",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/owasp-top-10.md",
          "tests/fixtures/scale_docs/owasp-top-10.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The OWASP Top 10 2021 is a list of the most critical security risks to web applications, as identified by the Open Web Application Security Project (OWASP). The top vulnerabilities include injection flaws (such as SQL injection and OS command injection), broken authentication, cross-site scripting (XSS), security misconfiguration, and insecure deserialization. To prevent these vulnerabilities, the document recommends techniques like input validation, using parameterized queries, applying the principle of least privilege, and leveraging web application firewalls and dynamic query builders.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 0,
          "combined": 0.0
        },
        "tokens_in_context": 233,
        "worker_latency_ms": 2013.1668329704553,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "AVX-512 vector instructions",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/vector-databases.md",
          "tests/fixtures/scale_docs/design-patterns-behavioral.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 0,
          "combined": 0.0
        },
        "tokens_in_context": 1167,
        "worker_latency_ms": 7176.647541928105,
        "judge_raw_response": "no reference answer"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "TCP/IP layer 4",
        "category": "lexical",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md",
          "tests/fixtures/scale_docs/load-balancing.md"
        ],
        "worker_answer": "Transport Layer",
        "reference_answer": "The Transport Layer in the TCP/IP protocol stack corresponds to layer 4 of the OSI model. This layer is responsible for ensuring complete data transfer between applications, including segmentation, flow control, and error correction. The two main protocols at the Transport Layer are TCP (Transmission Control Protocol) and UDP (User Datagram Protocol). TCP provides reliable, connection-oriented data transfer, while UDP offers unreliable, connectionless data transfer.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 5855,
        "worker_latency_ms": 16922.38925001584,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "IPv6 address format",
        "category": "lexical",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "An IPv6 address is a 128-bit address used to identify devices on an IPv6 network. The format of an IPv6 address consists of eight groups of four hexadecimal digits separated by colons, such as `2001:0db8:85a3:0000:0000:8a2e:0370:7334`. The first 64 bits represent the network prefix, and the last 64 bits represent the interface identifier, which is typically derived from the device's MAC address.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 0,
          "combined": 0.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 817.7224589744583,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "UUID v4 generation",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/data-pipeline-design.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "UUID v4 is a type of Universally Unique Identifier (UUID) that is randomly generated. The UUID v4 specification defines a method for generating UUIDs from random or pseudo-random numbers. This method produces a 128-bit number that is very likely to be unique across space and time. UUIDs generated using this method do not require a central registration authority and can be generated without consultation with that authority.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 764,
        "worker_latency_ms": 4764.089458039962,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "0xEF hex code",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The hexadecimal code 0xEF does not have a specific meaning in the context of cryptography. Hexadecimal codes are used to represent binary data, but 0xEF is not a commonly used value in cryptographic algorithms or standards. The source text does not mention the hexadecimal code 0xEF or provide any information about its meaning or usage in cryptography.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 736.8050419026986,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "Python 3.10 match statement",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The Python 3.10 match statement is a new pattern matching feature introduced in Python 3.10. It allows you to match a value against a series of patterns, similar to a switch statement in other programming languages. The match statement provides a concise and expressive way to handle complex data structures and control flow in your Python code.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 0,
          "combined": 0.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 739.0098340110853,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "ISO 27001 compliance",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/compliance-frameworks.md"
        ],
        "worker_answer": "ISO 27001 is an information security management system (ISMS) standard that provides a framework for establishing, implementing, maintaining, and continually improving an organization's information security management system. Compliance with ISO 27001 involves understanding the organization and its context, demonstrating leadership commitment to the ISMS, conducting risk assessments, planning actions to address risks and opportunities, providing necessary resources for support, implementing operational controls, evaluating performance of the ISMS, and continuously improving it. To become ISO 27001 certified, organizations must establish an ISMS based on the standard's requirements, conduct a comprehensive risk assessment and implement controls to mitigate risks, undergo an external audit by an accredited certification body, and maintain the ISMS for annual certification renewal.",
        "reference_answer": "ISO 27001 is an international standard that specifies the requirements for an information security management system (ISMS). To achieve ISO 27001 compliance, organizations must implement a comprehensive set of security controls across 14 domains, including information security policies, access control, cryptography, physical security, operations security, and incident management. The standard requires organizations to conduct regular risk assessments, implement appropriate security measures, and undergo third-party audits to verify the effectiveness of their ISMS.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 6184,
        "worker_latency_ms": 28181.034750072286,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "SOC2 Type II audit",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/compliance-frameworks.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/scale_docs/compliance-frameworks.md",
          "tests/fixtures/scale_docs/compliance-frameworks.md",
          "tests/fixtures/scale_docs/compliance-frameworks.md"
        ],
        "worker_answer": "SOC 2 Type 2 evaluates the design and operating effectiveness of an organization's controls over a period of time (typically 6-12 months).",
        "reference_answer": "A SOC 2 Type II audit evaluates the design and operating effectiveness of an organization's controls related to security, availability, processing integrity, confidentiality, and privacy of data over a period of time, typically 6-12 months. To achieve SOC 2 compliance, organizations must implement controls across these 5 trust principles, including access management, logging and monitoring, change management, vulnerability management, incident response, backup and disaster recovery, risk assessment, vendor management, and security awareness training.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 4805,
        "worker_latency_ms": 18793.787291040644,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "BGE-small-en-v1.5 model",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/scale_docs/container-security.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about a \"BGE-small-en-v1.5 model\".",
        "reference_answer": "The bge-small-en-v1.5 model is the embedding model used by the Candlekeep RAG (Retrieval-Augmented Generation) knowledge base server. Candlekeep uses this model as part of its \"Arcane Recall\" component within the RAG pipeline, which is responsible for the retrieval of relevant information from the underlying document database.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 1,
          "combined": 0.4286
        },
        "tokens_in_context": 1862,
        "worker_latency_ms": 12872.900333022699,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "ChromaDB v0.4.0",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/vector-databases.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/data-pipeline-design.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "ChromaDB v0.4.0 is not mentioned in the given source document. The document discusses the design of the Candlekeep system, which provides a RAG knowledge base that an AI agent can query. It describes the design goals and decisions, such as having three search paths (simple, hybrid, and precise), having the agent decompose queries rather than the search tool, and using \"Arcane Recall\" to return adjacent chunks around each match. However, there is no information about the specific ChromaDB version 0.4.0.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 3156,
        "worker_latency_ms": 18377.978042000905,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "MS-MARCO MiniLM L-6-v2",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The MS-MARCO MiniLM L-6-v2 is a technique that is part of the \"Recommended Stack\" for the RAG (Retrieval Augmented Generation) system optimization project described in the source document. Specifically, the document states that the \"Tier 1 (Always Use)\" recommendation is to use the \"Bardic Knowledge\" and \"Arcane Recall\" techniques, which together achieve a precision of 97.3%, content quality of 88.7%, and latency of 37ms. The \"Tier 2 (Conditional)\" recommendation is to use the \"Flurry of Blows\" technique for complex queries, if the additional latency of around 1100ms is acceptable.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 0,
          "combined": 0.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 791.4075840963051,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "JWT RS256 signing",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/sample_docs/authentication.md",
          "tests/fixtures/scale_docs/api-gateway-patterns.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "JWT (JSON Web Tokens) use the RS256 algorithm for signing. This is an asymmetric cryptographic algorithm that uses a private key to sign the token and a public key to verify it. The JWT structure consists of a header, payload, and signature. The header specifies the algorithm used, the payload contains the claims, and the signature is generated using the private key to ensure the integrity of the token. This allows for stateless authentication, where the server does not need to store session information.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 413,
        "worker_latency_ms": 3172.5635420298204,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "OAuth 2.0 PKCE flow",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/api-security.md",
          "tests/fixtures/scale_docs/api-security.md",
          "tests/fixtures/scale_docs/api-security.md"
        ],
        "worker_answer": "I don't know.\n\nThe provided context does not contain information about the OAuth 2.0 PKCE (Proof Key for Code Exchange) flow specifically, although it mentions that the guide covers PKCE. The context does not provide details on how to implement or use this flow.",
        "reference_answer": "The OAuth 2.0 PKCE (Proof Key for Code Exchange) flow is a security enhancement to the standard OAuth 2.0 authorization code grant flow. It is designed to protect against authorization code interception attacks, particularly in public clients like mobile apps or single-page applications (SPAs).\n\nIn the PKCE flow, the client application generates a cryptographically random \"code verifier\" value and derives a \"code challenge\" from it. The client sends the code challenge to the authorization server when requesting an authorization code. When the client later exchanges the authorization code for an access token, it also sends the original code verifier. The authorization server verifies that the code verifier matches the previously sent code challenge, ensuring that the token exchange is performed by the same client that initiated the authorization request.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 5931,
        "worker_latency_ms": 21579.15908296127,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "RAID 6 configurations",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/linux-filesystem.md",
          "tests/fixtures/scale_docs/cloud-design-patterns.md",
          "tests/fixtures/scale_docs/linux-filesystem.md",
          "tests/fixtures/scale_docs/linux-filesystem.md",
          "tests/fixtures/scale_docs/linux-filesystem.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "RAID 6 configurations provide data redundancy by using two independent parity blocks. This allows the system to continue operating even if two disks fail simultaneously. RAID 6 can tolerate the loss of any two disks in the array without data loss, making it a robust storage solution for mission-critical applications.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 5756,
        "worker_latency_ms": 20815.20295899827,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "SQL-92 standards",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/database-design.md",
          "tests/fixtures/scale_docs/monitoring-observability.md",
          "tests/fixtures/scale_docs/sql-query-optimization.md",
          "tests/fixtures/scale_docs/database-migrations.md",
          "tests/fixtures/scale_docs/data-quality.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about SQL-92 standards.",
        "reference_answer": "SQL-92 refers to the SQL standard published in 1992, which was the third major revision of the SQL language. This standard introduced several key features, including:\n\n- Support for outer joins, which allow for the inclusion of rows that do not have matching values in the joined tables.\n- Improved support for subqueries, including the ability to use subqueries in the SELECT, FROM, and WHERE clauses.\n- Standardized syntax for common SQL operations, such as CREATE TABLE, ALTER TABLE, and DROP TABLE.\n- Improved support for data types, including the introduction of the TIMESTAMP data type.\n\nThe SQL-92 standard laid the foundation for the modern SQL language and is still widely used today, even as newer versions of the standard have been published.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 3175,
        "worker_latency_ms": 18432.175624999218,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "POSIX thread management",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/linux-performance-tuning.md",
          "tests/fixtures/scale_docs/concurrency-patterns.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/concurrency-patterns.md",
          "tests/fixtures/scale_docs/concurrency-patterns.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about POSIX thread management.",
        "reference_answer": "POSIX thread management in Linux is handled through the use of the `fork()` and `exec()` system calls. The `fork()` system call creates a new process, which is a copy of the calling process, and the `exec()` system call replaces the current process image with a new process image. Processes in Linux can be in one of several states, including running, waiting, stopped, and zombie. The current state of processes can be viewed using the `ps` command.",
        "score": {
          "correctness": 0,
          "completeness": 1,
          "hallucination": 1,
          "combined": 0.2857
        },
        "tokens_in_context": 3500,
        "worker_latency_ms": 18486.706082941964,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 1, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "systemd unit files",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/systemd-services.md"
        ],
        "worker_answer": "Systemd uses \"unit files\" to manage services, sockets, mounts, devices, and other system resources. Unit files are configuration files that define how a particular service or resource should be managed. They typically have extensions like `.service`, `.socket`, `.target`, `.timer`, etc., and are usually located in `/etc/systemd/system/` or `/usr/lib/systemd/system/`.",
        "reference_answer": "Systemd unit files are configuration files used by the systemd init system to manage services, sockets, mounts, devices, and other system resources on Linux. Unit files are typically located in the /etc/systemd/system/ and /usr/lib/systemd/system/ directories, and have extensions like .service, .socket, .target, or .timer depending on the type of unit.\n\nUnit files define how a particular service or resource should be managed, including the command to start/stop the service, dependencies on other units, and whether the service should be automatically restarted. Systemd also uses \"targets\" to group related services and resources together, similar to the runlevels used in older init systems.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 3939,
        "worker_latency_ms": 21800.797292031348,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "EXT4 filesystem limits",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/linux-filesystem.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/cloud-storage-options.md",
          "tests/fixtures/scale_docs/linux-performance-tuning.md",
          "tests/fixtures/scale_docs/linux-filesystem.md"
        ],
        "worker_answer": "The maximum number of inodes is set during filesystem creation and cannot be changed later.",
        "reference_answer": "The ext4 filesystem has the following limits:\n\n- Maximum file size: 16TB\n- Maximum filesystem size: 1EB (exabyte)\n- Maximum number of subdirectories within a directory: 64,000\n- Maximum number of hard links to a file: 65,000\n\nThese limits are defined by the ext4 filesystem design and cannot be changed after the filesystem is created.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 4491,
        "worker_latency_ms": 18496.976084075868,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "YAML 1.2 syntax",
        "category": "lexical",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/helm-charts.md",
          "tests/fixtures/scale_docs/api-gateway-patterns.md",
          "tests/fixtures/scale_docs/configuration-management.md",
          "tests/fixtures/scale_docs/container-fundamentals.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about YAML 1.2 syntax.",
        "reference_answer": "YAML 1.2 syntax is the version of the YAML data serialization format used by Ansible. Ansible Playbooks, which define the configuration management tasks to be executed, are written in YAML 1.2 format. The YAML syntax used in Ansible Playbooks follows a specific structure, including sections for hosts, tasks, handlers, variables, and templates. Ansible Playbooks are executed using the `ansible-playbook` command.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 1005,
        "worker_latency_ms": 7140.2912909397855,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "Docker Compose v2",
        "category": "lexical",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/container-fundamentals.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/dependency-injection.md",
          "tests/fixtures/scale_docs/dependency-injection.md",
          "tests/fixtures/scale_docs/container-fundamentals.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about Docker Compose v2. It only describes what Docker Compose is and provides an example of a `docker-compose.yml` file.",
        "reference_answer": "Docker Compose v2 is a newer version of the Docker Compose tool, which is used to define and run multi-container Docker applications. The key components that enable container technology, as described in the source text, are namespaces, control groups (cgroups), and union file systems (OverlayFS). These Linux kernel features provide isolation, resource management, and efficient layering of file systems for containers.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 4213,
        "worker_latency_ms": 18859.578749979846,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "AWS IAM policies",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/secrets-management.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/well-architected-framework.md"
        ],
        "worker_answer": "AWS IAM policies can be used to grant or deny access to specific secrets or groups of secrets in Secrets Manager. For example, an IAM policy can allow read access to a specific secret as shown below:\n\n```json\n{\n  \"Version\": \"2012-10-17\",\n  \"Statement\": [\n    {\n      \"Effect\": \"Allow\",\n      \"Action\": [\n        \"secretsmanager:GetSecretValue\",\n        \"secretsmanager:DescribeSecret\"\n      ],\n      \"Resource\": \"arn:aws:secretsmanager:<region>:<account-id>:secret:<secret-name>\"\n    }\n  ]\n}\n```",
        "reference_answer": "AWS IAM (Identity and Access Management) policies are JSON-based documents that define the permissions for AWS resources. These policies specify the actions that are allowed or denied on specific resources, such as Amazon S3 buckets, Amazon EC2 instances, or AWS Lambda functions. IAM policies can be attached to IAM users, groups, or roles to control their access to AWS services and resources. They are a fundamental component of AWS security and access management, allowing organizations to implement the principle of least privilege and grant only the necessary permissions to their AWS entities.",
        "score": {
          "correctness": 2,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.7143
        },
        "tokens_in_context": 1308,
        "worker_latency_ms": 13968.924832995981,
        "judge_raw_response": "{\"correctness\": 2, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "how to implement chaos engineering",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/chaos-engineering.md"
        ],
        "worker_answer": "To implement chaos engineering, follow these steps based on the provided context:\n\n1. Define a hypothesis about your system's behavior under failure conditions.\n2. Run experiments continuously, not just during initial development or before major releases.\n3. Ensure experiments are conducted in environments that closely mimic production conditions.\n4. Gradually increase the scale and impact of experiments as confidence in system resilience grows.\n5. Use fault injection techniques such as network latency, resource exhaustion, service failures, dependency failures, and Chaos Monkey.\n6. Conduct blameless postmortems to analyze experiment outcomes and identify areas for improvement.\n7. Schedule regular game days involving cross-functional teams to test resilience and foster a culture of continuous improvement.\n8. Define clear steady state hypotheses that specify expected system behavior under normal conditions and verify against these during experiments.\n9. Carefully manage the blast radius of each experiment to control its impact and avoid unintended disruptions.\n\nThese steps are based on the principles outlined in the provided document about chaos engineering.",
        "reference_answer": "To implement chaos engineering, you can follow these key principles and techniques:\n\n1. Conduct hypothesis-driven experiments to test the system's resilience under turbulent conditions. Clearly define a testable hypothesis about the system's behavior.\n\n2. Run chaos experiments continuously, not just during initial development or before major releases. Resilience must be an ongoing concern.\n\n3. Use controlled environments that closely mimic production to ensure representative results. Start with small, low-impact experiments and gradually increase the blast radius.\n\n4. Inject faults like network latency, resource exhaustion, service failures, and dependency failures to observe how the system responds. Tools like the Chaos Toolkit can help automate these fault injection experiments.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 4055,
        "worker_latency_ms": 25844.79750005994,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "benefits of event-driven architecture",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md"
        ],
        "worker_answer": "The benefits of event-driven architecture include:\n\n1. Loose Coupling: Components are decoupled from each other, allowing for easier maintenance, independent scaling, and more flexibility.\n2. Asynchronous Communication: Events can be processed asynchronously, improving the overall responsiveness and scalability of the system.\n3. Scalability: Event-driven architectures can scale individual components independently, as they are decoupled from each other.\n4. Fault Tolerance: If one component fails, the rest of the system can continue to function, improving the overall resilience of the system.\n5. Audit Trail: The event store provides a comprehensive audit trail of all the actions and state changes that have occurred in the system.",
        "reference_answer": "The key benefits of event-driven architecture include:\n\n1. Loose coupling: Components are decoupled, allowing for easier maintenance, independent scaling, and more flexibility.\n2. Asynchronous communication: Events can be processed asynchronously, improving responsiveness and scalability.\n3. Scalability: Individual components can scale independently due to the decoupled nature of the architecture.\n4. Fault tolerance: If one component fails, the rest of the system can continue to function, improving overall resilience.\n5. Audit trail: The event store provides a comprehensive record of all actions and state changes in the system.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 4938,
        "worker_latency_ms": 23338.75016705133,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "serverless vs containers",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/serverless-architecture.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information comparing serverless architecture to containers.",
        "reference_answer": "Serverless architecture and containers are two different approaches to cloud computing. Serverless architecture, exemplified by AWS Lambda, is a cloud computing execution model where the cloud provider is responsible for executing code and managing the underlying infrastructure. In this model, developers focus on writing code without worrying about provisioning or managing servers. Containers, on the other hand, are a way to package and run applications in a consistent, isolated environment, allowing for portability and scalability across different computing environments.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 1061,
        "worker_latency_ms": 6519.59091599565,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "microservices data consistency",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/nosql-patterns.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md"
        ],
        "worker_answer": "Data consistency in microservices can be achieved using patterns like the Saga Pattern for distributed transactions and Event Sourcing for maintaining an append-only event log to rebuild state. These patterns help manage consistency across services in a microservices architecture where traditional ACID transactions might be too restrictive or difficult to achieve.",
        "reference_answer": "",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 0,
          "combined": 0.0
        },
        "tokens_in_context": 1538,
        "worker_latency_ms": 10677.609457983635,
        "judge_raw_response": "no reference answer"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "data lakehouse architecture",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-lake-architecture.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain specific information about data lakehouse architecture, although it mentions \"lakehouse patterns\" in the description. For a detailed explanation of data lakehouse architecture, additional context or information would be required.",
        "reference_answer": "The data lakehouse architecture combines the flexibility and scalability of a data lake with the structured data management capabilities of a data warehouse. Key components include:\n\n1. Ingestion Layer: Responsible for bringing data into the data lake from various sources, using tools like Apache Kafka, Apache Spark Streaming, AWS Kinesis, and Azure Event Hubs.\n\n2. Storage Layer: The foundation of the data lake, where raw, unprocessed data is stored using object storage services like Amazon S3, Azure Blob Storage, or Google Cloud Storage.\n\n3. Processing Layer: Responsible for transforming and enriching the raw data stored in the data lake, using tools like Apache Spark, Apache Flink, AWS Glue, or Azure Databricks for batch and stream processing.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 5509,
        "worker_latency_ms": 21638.42583296355,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "zero trust security principles",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md"
        ],
        "worker_answer": "Zero trust security principles include:\n\n- Seamless identity management, micro-segmentation, and continuous monitoring across hybrid and multi-cloud environments.\n- API-driven automation and orchestration for scalable implementation of security controls.\n- Integration with existing security tools to maximize their value.\n- A phased approach for transitioning to zero trust, starting with critical assets and expanding iteratively.\n- Comprehensive workforce education and change management for buy-in across the organization.\n\nAdditionally, zero trust emphasizes least privilege access at both network and application levels, continuous validation and monitoring of activities, and dynamic adjustment of access privileges based on real-time risk assessments.",
        "reference_answer": "The core principles of zero trust security include:\n\n1. Verify Explicitly: All users, devices, and applications must be continuously authenticated and authorized, regardless of network location.\n2. Use Least Privilege Access: Granular access policies should be defined to grant the minimum necessary privileges for each user, device, or application to perform their required functions.\n3. Assume Breach: Organizations should assume that a breach has already occurred and design security controls accordingly, focusing on data protection and rapid response.\n4. Verify and Monitor Everywhere: All network traffic, user activities, and system events should be continuously monitored and validated, both on-premises and in the cloud.\n\nImplementing a zero trust architecture involves key components such as identity verification and access control, micro-segmentation and least privilege, and continuous validation and monitoring.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 1330,
        "worker_latency_ms": 12500.876541947946,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "continuous integration best practices",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know. The provided context does not contain information about continuous integration best practices.",
        "reference_answer": "The key continuous integration best practices based on the source text are:\n\n1. Establish a clear CI/CD pipeline with well-defined stages, including source code management, build, test, static code analysis, artifact storage, and deployment.\n2. Automate the pipeline stages using tools like Jenkins, with specific steps for compiling the source code, running tests, analyzing the code, storing artifacts, and deploying the application.\n3. Manage the generated artifacts, such as compiled binaries, Docker images, and test reports, by storing them in a repository for later use.\n4. Implement parallel execution of pipeline stages to improve efficiency and reduce the overall build time.\n5. Leverage caching mechanisms to speed up the pipeline by reusing previously built artifacts or test results.\n6. Adopt deployment strategies like blue-green, canary, or rolling deployments to ensure reliable and safe application updates.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 0,
          "combined": 0.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 1343.5030829859897,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "database sharding strategies",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/database-sharding.md",
          "tests/fixtures/sample_docs/database-design.md",
          "tests/fixtures/sample_docs/caching.md",
          "tests/fixtures/scale_docs/nosql-patterns.md",
          "tests/fixtures/sample_docs/caching.md"
        ],
        "worker_answer": "Database sharding strategies include:\n\n1. Hash-based sharding - Uses a hash function to determine the shard for data items, ensuring even distribution across shards but potentially leading to uneven distribution if data is not uniformly distributed. Pros include load balancing and transparent application treatment of the database, while cons involve complexity in cross-shard queries and challenges in resharding.\n\n2. Range-based sharding - Partitions data based on ranges of values, suitable for naturally divisible data like dates or geographic regions. Pros include intuitive management and efficient cross-shard queries, but cons involve potential for uneven data distribution and hotspots.\n\n3. Directory-based sharding - Uses a centralized directory to map data items to specific shards, offering flexibility and easier resharding but introducing additional complexity and potential for a bottleneck.\n\nCross-shard queries can be handled through scatter-gather, routed queries, or materialized views. Resharding strategies include shard splitting, merging, and rebalancing to manage changes in data or workload.",
        "reference_answer": "Database sharding strategies refer to the different approaches used to horizontally partition data across multiple database instances, or \"shards\", to improve scalability and performance. The three main sharding strategies are:\n\n1. Hash-based sharding: This uses a hash function to determine the shard where a particular data item is stored, based on a specific column or set of columns. It results in even data distribution but can make cross-shard queries more complex.\n\n2. Range-based sharding: This partitions data based on a range of values in a specific column or set of columns, often used when data can be naturally divided into logical ranges. It can enable more efficient cross-shard queries but may lead to uneven data distribution.\n\n3. Directory-based sharding: This uses a central directory to map data to specific shards, allowing more flexibility in shard management but introducing additional complexity and a potential single point of failure.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 4169,
        "worker_latency_ms": 27749.647791031748,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "load balancing algorithms",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/load-balancing.md",
          "tests/fixtures/scale_docs/load-balancing.md"
        ],
        "worker_answer": "Load balancing algorithms are the core logic that determines how incoming traffic is distributed across a pool of backend servers. Common load balancing algorithms include:\n\n- Round-robin: Sends each new connection to the next server in the list, cycling through the entire pool.\n- Least connections: Sends each new connection to the server with the fewest number of active connections.\n- Weighted: Allows assigning a weight value to each backend server, directing more traffic to servers with higher weights.\n- IP hash: Uses a hash of the client's IP address to determine which server the connection should be sent to, ensuring that a client is always sent to the same backend server.",
        "reference_answer": "The most common load balancing algorithms are:\n\nRound-robin: Sends each new connection to the next server in the list, cycling through the entire pool to ensure each server receives an equal number of connections.\n\nLeast connections: Sends each new connection to the server with the fewest number of active connections, helping to balance the load more evenly across backend servers.\n\nWeighted: Allows assigning a weight value to each backend server, so servers with a higher weight receive more traffic than those with a lower weight, useful for servers with different capacities.\n\nIP hash: Uses a hash of the client's IP address to determine which server the connection should be sent to, maintaining session affinity.\n\nLoad balancers can operate at different layers of the OSI model, with layer 4 load balancers making decisions based on transport layer information like IP addresses and ports, while layer 7 load balancers have more visibility into the application layer.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 4035,
        "worker_latency_ms": 25012.524583027698,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "content delivery network optimization",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "A Content Delivery Network (CDN) is designed to efficiently deliver content to users around the world. The key components of a CDN architecture include edge servers, which are strategically placed to minimize the distance and latency between the user and the content, and an origin server, which is the primary source of the content. Load balancers direct user requests to the appropriate edge server, and a cache management system manages the content stored on the edge servers. Edge caching is a fundamental aspect of CDN architecture, where content is stored on the edge servers closest to the end-users, allowing the CDN to serve content directly from the edge and reduce latency and load on the origin server.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 0,
          "combined": 0.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 812.9348749062046,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "service mesh advantages",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [],
        "worker_answer": "I don't know. The provided context does not contain information about the advantages of a service mesh.",
        "reference_answer": "The key advantages of a service mesh architecture include:\n\n1. Traffic management: A service mesh provides advanced traffic control features such as load balancing, circuit breaking, retries, and timeouts.\n\n2. Security: The service mesh can enable secure communication between services using mutual TLS (mTLS) for authentication and encryption.\n\n3. Observability: The service mesh collects telemetry data, including metrics, logs, and traces, to provide visibility into the health and performance of the mesh.\n\n4. Decoupling of application and infrastructure concerns: The service mesh architecture separates the application code from the infrastructure concerns, allowing developers to focus on building business logic while the service mesh handles cross-cutting concerns.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 1516.732707968913,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "infrastructure as code tools",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/infrastructure-as-code.md",
          "tests/fixtures/scale_docs/infrastructure-as-code.md",
          "tests/fixtures/scale_docs/infrastructure-as-code.md"
        ],
        "worker_answer": "The provided context specifically discusses Terraform, which is an infrastructure as code tool. It does not mention other infrastructure as code tools directly. Therefore, based on the given context:\n\n- Infrastructure as code tool: Terraform",
        "reference_answer": "Terraform is an open-source Infrastructure as Code (IaC) tool that allows you to define and provision your infrastructure in a declarative manner. It supports a wide range of cloud providers and on-premises resources. Terraform maintains the state of your infrastructure in a state file, which is typically stored in a backend, such as a local file, an S3 bucket, or a HashiCorp Consul backend. Terraform supports state locking to prevent multiple users or processes from modifying the state file at the same time, and state importing to add existing resources to the Terraform state.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 5161,
        "worker_latency_ms": 17585.736542008817,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "monitoring vs observability",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/monitoring-observability.md",
          "tests/fixtures/scale_docs/monitoring-observability.md",
          "tests/fixtures/scale_docs/monitoring-observability.md"
        ],
        "worker_answer": "Monitoring involves tracking specific metrics and events to understand the health and performance of a system. Observability, on the other hand, is about making it possible to gain insights into the internal state of a system by analyzing its outputs. While monitoring focuses on predefined metrics, observability enables deeper investigation into the underlying issues by leveraging logs, traces, and other data sources.",
        "reference_answer": "Monitoring and observability are related but distinct concepts. Monitoring focuses on collecting and analyzing metrics, such as the RED (Rate, Errors, Duration) and USE (Utilization, Saturation, Errors) metrics, to track the health and performance of a system. Observability, on the other hand, is a broader concept that encompasses monitoring, as well as other techniques like distributed tracing and log aggregation, to provide a more comprehensive understanding of a system's behavior and enable faster issue resolution.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 7068,
        "worker_latency_ms": 18913.57841703575,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "auto scaling in the cloud",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cost-optimization.md",
          "tests/fixtures/scale_docs/auto-scaling.md"
        ],
        "worker_answer": "Auto-scaling is a technique for dynamically adjusting cloud resources to match workload demands. It involves automatically scaling resources up or down based on predefined policies and metrics like CPU utilization, memory usage, or queue length. This ensures you only pay for the resources needed at any given time and helps maintain optimal performance.",
        "reference_answer": "Auto-scaling in the cloud refers to the ability of a system to automatically adjust its resource capacity based on actual load. There are several strategies for implementing auto-scaling, including:\n\n1. Target Tracking Scaling: This adjusts the capacity to maintain a target value for a specific metric, such as average CPU utilization, keeping the target metric as close to the specified value as possible.\n\n2. Step Scaling: This adjusts the capacity based on a set of scaling adjustments, known as step adjustments, which specify a metric value range and the corresponding adjustment to make to the current capacity.\n\n3. Predictive Scaling: This uses load forecasting to automatically scale the capacity ahead of predicted traffic changes, allowing the system to proactively scale resources to meet anticipated demand.",
        "score": {
          "correctness": 2,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.7143
        },
        "tokens_in_context": 2891,
        "worker_latency_ms": 19828.84583401028,
        "judge_raw_response": "{\"correctness\": 2, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "disaster recovery planning",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/disaster-recovery.md",
          "tests/fixtures/scale_docs/database-replication.md"
        ],
        "worker_answer": "Disaster recovery planning involves implementing strategies to recover IT systems, data, and operations after a disaster or major outage. Key elements include defining RPO (Recovery Point Objective) and RTO (Recovery Time Objective), choosing between strategies like backup and restore, pilot light, warm standby, or multi-site active-active architectures, and ensuring these strategies align with business requirements for data loss tolerance and downtime acceptance.",
        "reference_answer": "Disaster recovery planning involves strategies to recover IT systems, data, and operations in the event of a disaster or major outage. The two key metrics are Recovery Point Objective (RPO), the maximum amount of data that can be lost, and Recovery Time Objective (RTO), the maximum time before normal operations must resume. A fundamental strategy is backup and restore, which involves regularly creating full or incremental backups of critical data and systems that can be used to restore operations. Backups can be stored on-site, off-site, or in the cloud, and the specific backup strategy should be designed to meet the organization's RPO and RTO requirements.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 1669,
        "worker_latency_ms": 12650.734875001945,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "api gateway patterns",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/api-gateway-patterns.md",
          "tests/fixtures/scale_docs/api-gateway-patterns.md"
        ],
        "worker_answer": "API Gateway Patterns include:\n\n1. Proxy Pattern - forwards requests directly to the appropriate microservice without additional processing.\n2. Aggregator Pattern - aggregates responses from multiple microservices and returns a combined result to the client.\n3. Translator Pattern - translates requests and responses between different data formats or protocols.\n4. Decomposer Pattern - decomposes client requests into individual service calls, executes them concurrently, and composes the final response.",
        "reference_answer": "The API gateway design patterns described in the document include:\n\n1. Proxy Pattern: The API gateway forwards requests directly to the appropriate microservice without any additional processing. This is suitable for simple use cases.\n\n2. Aggregator Pattern: The API gateway sends individual requests to relevant services, aggregates the responses, and returns the combined result to the client. This is used when a client needs to retrieve data from multiple microservices.\n\n3. Translator Pattern: The API gateway is responsible for translating the client's request into the format expected by the microservice, and then translating the response back into the client's expected format. This is used when the client and microservice APIs have different data formats or protocols.\n\n4. Decomposer Pattern: The API gateway decomposes the client's request into individual service calls, executes them concurrently, and then composes the final response. This is used when a client request requires data from multiple microservices, but the client does not want to be responsible for orchestrating those requests.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 5195,
        "worker_latency_ms": 20211.382457986474,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "cloud storage options",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cloud-storage-options.md",
          "tests/fixtures/scale_docs/cost-optimization.md",
          "tests/fixtures/scale_docs/cloud-storage-options.md",
          "tests/fixtures/scale_docs/cloud-storage-options.md"
        ],
        "worker_answer": "The document discusses cloud storage options including object storage (Amazon S3), block storage (Amazon EBS), and file storage (Amazon EFS). It covers comparisons, lifecycle policies, and access patterns for these services.",
        "reference_answer": "Amazon S3 is an object storage service that offers several storage classes for different data access patterns and durability requirements. S3 Standard is for frequently accessed data, S3 Intelligent-Tiering automatically moves data between access tiers, S3 Standard-IA and S3 One Zone-IA are for infrequently accessed data, and S3 Glacier and S3 Glacier Deep Archive are for long-term archival of rarely accessed data. S3 Lifecycle policies can be used to automatically transition objects between storage classes or delete objects based on age. S3 supports various access methods like GET/HEAD/PUT/DELETE, multi-part uploads, batch operations, and data querying with S3 Select and S3 Glacier Select.",
        "score": {
          "correctness": 2,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.7143
        },
        "tokens_in_context": 4349,
        "worker_latency_ms": 19241.246999939904,
        "judge_raw_response": "{\"correctness\": 2, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "data serialization formats",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-serialization.md"
        ],
        "worker_answer": "The document discusses several popular data serialization formats, including JSON, Protobuf, Avro, Parquet, and ORC. It compares their performance, schema evolution capabilities, and usage scenarios to help in choosing the right format for specific needs.",
        "reference_answer": "Data serialization formats refer to the various ways of converting data structures or objects into a format that can be stored or transmitted, and then reconstructed later. Some popular data serialization formats include:\n\nJSON (JavaScript Object Notation): A lightweight, human-readable data interchange format that represents data as a collection of name/value pairs and ordered lists of values. It has a simple syntax, is language-independent, and is widely adopted, but can be verbose and has limited data types.\n\nProtobuf (Protocol Buffers): A binary serialization format developed by Google that is compact, fast, and language-neutral. It uses a schema definition language to define the structure of the data, enabling efficient schema evolution.\n\nAvro: A row-oriented data serialization system developed by Apache that provides rich data structures, a compact binary format, and dynamic typing. It supports schema evolution and is often used in big data and stream processing applications.\n\nParquet and ORC: Column-oriented file formats designed for efficient storage and querying of large datasets. They use compression and encoding techniques to achieve high performance and are commonly used in big data ecosystems like Hadoop and Spark.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 7077,
        "worker_latency_ms": 20082.48937490862,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "message queue patterns",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cloud-design-patterns.md",
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/concurrency-patterns.md",
          "tests/fixtures/scale_docs/error-handling-patterns.md",
          "tests/fixtures/sample_docs/microservices.md"
        ],
        "worker_answer": "The provided context discusses several message queue patterns including:\n\n1. Queue-based Load Leveling Pattern: This pattern smooths out high volumes of requests or tasks by placing them in a queue. It allows the system to handle bursts of activity without overloading downstream components.\n\n2. Competing Consumers Pattern: This pattern allows multiple concurrent consumers to process messages from a queue or message broker, which can help improve the throughput and scalability of systems that need to process high volumes of messages.\n\nThese patterns are part of a broader set of cloud design patterns aimed at building resilient applications.",
        "reference_answer": "Message queue patterns are a key component of event-driven architecture. The document describes the following relevant patterns:\n\n1. Event Producers: Components that generate events based on actions or state changes.\n2. Event Consumers: Components that subscribe to and process events.\n3. Event Bus: The middleware that facilitates the publishing and subscribing of events.\n\nThese patterns allow for asynchronous, decoupled communication between components in an event-driven system, promoting scalability, fault tolerance, and flexibility.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 4761,
        "worker_latency_ms": 21176.110000000335,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "functional programming concepts",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/functional-programming.md"
        ],
        "worker_answer": "Functional Programming Concepts include:\n\n- **Immutability**: Values cannot be changed once created; instead, new values are created based on existing ones.\n- **Pure Functions**: Functions that return the same output for the same input and have no side effects.\n- **Monads**: Design patterns used to manage computational effects like error handling, optional values, and asynchronous operations.\n- **Functors**: Data structures that can be mapped over to apply functions to their values, creating new functors with transformed values.\n- **Pattern Matching**: Technique for decomposing and extracting data from complex structures, providing a concise way to handle different cases.\n- **Algebraic Data Types (ADTs)**: Custom data types that can represent a wide range of domain-specific concepts, including sum types (tagged unions) and product types (records).",
        "reference_answer": "Functional programming concepts include:\n\n1. Immutability: In functional programming, values are immutable, meaning they cannot be changed once created. Instead, new data structures are created with the desired changes.\n\n2. Pure functions: Pure functions are functions that, given the same input, will always return the same output and have no side effects. They do not depend on or modify any external state.\n\nThe source text provides details on the benefits of immutability, such as referential transparency, concurrency, and easier debugging and testing. It also explains the benefits of pure functions, including referential transparency, testability, parallelism, and caching/memoization.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 3292,
        "worker_latency_ms": 25501.711291028187,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "linux memory management",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/linux-memory-management.md",
          "tests/fixtures/scale_docs/linux-performance-tuning.md",
          "tests/fixtures/scale_docs/linux-performance-tuning.md"
        ],
        "worker_answer": "Linux memory management involves several key components including virtual memory, page tables, swap space, the OOM killer, huge pages, and NUMA. It manages memory allocation, deallocation, and the translation of virtual addresses to physical addresses using page tables. When a process attempts to access memory, if the required data is not present in physical memory, it triggers a page fault. The kernel then handles this by loading the necessary data from disk or swap space, updating the page tables accordingly. This system ensures efficient use of memory resources and maintains system stability even when physical memory is scarce.",
        "reference_answer": "Linux memory management involves the use of virtual memory, which allows processes to access more memory than is physically available on the system. This is implemented through page tables, which map virtual memory pages to physical memory frames. The processor's Memory Management Unit (MMU) uses these page tables to translate virtual addresses to physical addresses during memory accesses. Page tables are organized in a multi-level hierarchy, with a top-level page directory and lower-level page tables. When a process attempts to access a virtual address, the MMU uses the page tables to translate the virtual address to a physical address. If the page is not present in the page tables, the MMU generates a page fault, which is handled by the kernel.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 7469,
        "worker_latency_ms": 23947.22270895727,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "dns resolution process",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/dns-resolution.md",
          "tests/fixtures/scale_docs/network-troubleshooting.md",
          "tests/fixtures/scale_docs/network-troubleshooting.md"
        ],
        "worker_answer": "The DNS resolution process involves several steps:\n\n1. **Client Lookup**: The client checks its local cache for the domain name's IP address.\n2. **Recursive Query**: If not found, the client sends a recursive query to its configured DNS resolver.\n3. **Iterative Queries**: The resolver queries the root DNS servers, TLD servers, and finally the authoritative DNS server for the domain.\n4. **Authoritative Server Lookup**: The resolver queries the authoritative DNS server to retrieve the IP address or other requested record.\n5. **Response to Client**: The resolver caches the result and sends it back to the client.\n\nThis process allows translation of domain names into IP addresses, enabling internet services like web browsing and email delivery.",
        "reference_answer": "The DNS resolution process involves the following steps:\n\n1. The client (e.g., web browser) first checks its local cache for the requested domain's IP address. If not found, it sends a recursive query to a DNS resolver.\n\n2. The resolver queries the root DNS servers to find the appropriate top-level domain (TLD) server, then queries the TLD server to locate the authoritative DNS server for the domain.\n\n3. The authoritative server provides the requested information, such as the IP address, which the resolver then caches and returns to the client.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 4772,
        "worker_latency_ms": 24691.95479096379,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "connection pooling benefits",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/connection-pooling.md"
        ],
        "worker_answer": "Connection pooling helps improve performance and scalability by managing database connections efficiently. It reduces the overhead of creating new connections for every operation, reusing existing connections instead. This leads to faster response times and better utilization of database resources.",
        "reference_answer": "Database connection pooling provides several benefits:\n\n1. Improved performance: By reusing existing database connections instead of creating new ones for every operation, connection pooling reduces the overhead and latency associated with establishing new connections.\n\n2. Increased scalability: Connection pooling allows an application to handle more concurrent requests by efficiently managing a limited number of database connections.\n\n3. Efficient resource utilization: Connection pooling optimizes the use of database server resources by maintaining a pool of reusable connections, reducing the need to create and destroy connections frequently.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 2947,
        "worker_latency_ms": 19016.671999939717,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "cryptography basics",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cryptography-basics.md"
        ],
        "worker_answer": "Cryptography fundamentals include:\n\n- **Symmetric Encryption**: Uses a single shared secret key for both encryption and decryption. Examples include AES (Advanced Encryption Standard) and ChaCha20.\n- **Asymmetric Encryption**: Uses a pair of keys, one public for encryption and one private for decryption. Examples include RSA (Rivest-Shamir-Adleman).\n- **Hashing**: Converts data of arbitrary size into a fixed-size string of bytes, typically for verifying the integrity or authenticity of data. Examples include SHA-256.\n- **Key Derivation Functions (KDFs)**: Generates cryptographic keys from a secret information like a password.",
        "reference_answer": "Cryptography is the practice of securing information through the use of codes and ciphers. The fundamental concepts of cryptography include:\n\n- Symmetric encryption: A single shared secret key is used to encrypt and decrypt data, such as the Advanced Encryption Standard (AES) algorithm. AES supports 128, 192, and 256-bit key sizes and operates on 128-bit blocks of data.\n- Asymmetric encryption: Two separate keys are used - a public key for encryption and a private key for decryption, such as RSA and ECDSA.\n- Hashing: A one-way mathematical function that converts data into a fixed-size output, such as SHA-256 and bcrypt.\n- Key derivation functions (KDFs): Algorithms that derive secure cryptographic keys from a password or other input, such as PBKDF2 and Argon2.\n\nThese fundamental cryptographic concepts are widely used to secure data, communications, and user authentication.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 6475,
        "worker_latency_ms": 27797.018917044625,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "distributed transactions",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "Distributed transactions refer to the coordination of multiple operations across different systems or databases as a single logical transaction. The source text does not directly discuss distributed transactions, as it focuses on concurrency patterns and constructs within a single system, such as thread pools. The text provides an example implementation of a basic thread pool in Python, but does not mention how to handle distributed transactions or the challenges involved in coordinating multiple systems. The source text does not contain information to answer the query about distributed transactions.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 818.3059999719262,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "container security hardening",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/network-security-hardening.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/scale_docs/container-security.md"
        ],
        "worker_answer": "Container security hardening includes several key practices such as image scanning, runtime protection, seccomp, AppArmor, rootless containers, and supply chain security. These measures help in identifying vulnerabilities, securing the runtime environment, and ensuring that container images are free from known issues.",
        "reference_answer": "Container security hardening involves several key practices:\n\n1. Image scanning: Scanning container images for known vulnerabilities using tools like Trivy, Clair, and Anchore, and scanning for malware using tools like ClamAV.\n2. Runtime protection: Implementing security measures like seccomp and AppArmor to restrict the capabilities of running containers and prevent unauthorized access or actions.\n3. Rootless containers: Using rootless containers, which run as non-root users, to reduce the risk of privilege escalation attacks.\n4. Supply chain security: Ensuring the security of the container image supply chain by verifying the provenance and integrity of container images.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 3690,
        "worker_latency_ms": 20079.300583922304,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "well architected framework",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/well-architected-framework.md"
        ],
        "worker_answer": "The AWS Well-Architected Framework consists of six pillars that help in designing and operating reliable, secure, efficient, and cost-effective systems on AWS. These pillars are:\n\n1. **Operational Excellence**: Focuses on running and monitoring systems to deliver business value, continually improving processes.\n2. **Security**: Involves protecting information, systems, and assets while managing risk.\n3. **Reliability**: Ensures the system can recover from disruptions, scale to meet demand, and mitigate failures.\n4. **Performance Efficiency**: Aims at using computing resources efficiently to meet system requirements and to maintain optimal cost.\n5. **Cost Optimization**: Focuses on running systems at the lowest price point possible while maintaining business value.\n6. **(Not explicitly named in the provided context but implied by structure) - Security**: Detailed technical documentation on security measures and best practices.\n\nThe provided context mainly elaborates on the Reliability pillar, including its design principles, key areas, and an example of implementation.",
        "reference_answer": "The AWS Well-Architected Framework is a set of best practices and design principles to help cloud architects build secure, high-performing, resilient, and efficient infrastructure. The Operational Excellence pillar focuses on the ability to run and monitor systems to deliver business value and continually improve supporting processes and procedures. Key design principles include automating operational procedures, embedding documentation, making frequent and reversible changes, and anticipating and learning from failures. The framework covers areas such as implementing strong change management, maintaining observability, having effective incident response, and continuously improving operations based on data.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 6309,
        "worker_latency_ms": 26835.459708003327,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "cost optimization strategies",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cost-optimization.md"
        ],
        "worker_answer": "The document outlines several cloud cost optimization strategies including right-sizing, reserved instances, spot instances, auto-scaling, and storage tiering. Right-sizing involves ensuring cloud resources are appropriately sized to match workload requirements without being oversized or undersized. Reserved Instances offer upfront commitments for lower hourly rates on resources, providing cost savings compared to on-demand pricing. Spot Instances are discounted excess compute capacity that can be interrupted, offering cost savings for flexible workloads. Auto-scaling dynamically adjusts resources based on workload demands to ensure efficient resource utilization. Storage tiering involves organizing data across different storage media based on access frequency and performance requirements to optimize costs.",
        "reference_answer": "Cloud cost optimization strategies include:\n\n1. Right-sizing: Ensuring cloud resources are appropriately sized to meet workload requirements. This involves identifying and resizing oversized resources to better match usage needs.\n\n2. Reserved Instances: Committing to a longer-term usage of cloud resources in exchange for a discounted hourly rate. This can provide significant cost savings compared to on-demand pricing.\n\n3. Spot Instances: Utilizing spare cloud computing capacity at discounted prices. Spot instances can offer substantial cost savings but are subject to interruption.\n\n4. Auto-scaling: Automatically scaling cloud resources up and down based on demand to optimize costs and ensure efficient resource utilization.\n\n5. Storage tiering: Leveraging different storage tiers (e.g., hot, warm, cold) to match data access requirements and minimize storage costs.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 3985,
        "worker_latency_ms": 23343.910124967806,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "incident response steps",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/incident-response.md"
        ],
        "worker_answer": "The incident response steps outlined in the provided context are:\n\n1. Detection: The initial identification of an incident.\n2. Triaging: Assessing the severity, impact, and urgency to determine the appropriate response.\n3. Escalation: Notifying relevant teams or on-call personnel.\n4. Remediation: Investigating, diagnosing, and resolving the incident.\n5. Postmortem: Reviewing the incident to identify root causes and document lessons learned.\n6. Closure: Formally closing the incident after necessary actions have been completed.",
        "reference_answer": "The key incident response steps outlined in the document are:\n\n1. Detection: Identifying the incident, typically through monitoring systems or user reports.\n2. Triaging: Assessing the incident's severity, impact, and urgency to determine the appropriate response.\n3. Escalation: Notifying the relevant teams or on-call personnel to initiate the incident response process.\n4. Remediation: Investigating, diagnosing, and resolving the incident.\n5. Postmortem: Reviewing the incident, identifying root causes, and documenting lessons learned.\n6. Closure: Formally closing the incident after all necessary actions have been completed.\n\nThe document also describes four incident severity levels, ranging from critical (Severity 1) to low (Severity 4), which determine the response and prioritization of the incident.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 5549,
        "worker_latency_ms": 21190.31712494325,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "multi-region deployment",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/multi-region-deployment.md"
        ],
        "worker_answer": "A multi-region deployment strategy involves deploying applications and services across multiple geographic regions to improve availability, reduce latency, and provide disaster recovery capabilities. This guide covers active-active vs active-passive architectures, data replication techniques (synchronous, asynchronous, eventual consistency), and DNS failover mechanisms. Each approach has tradeoffs related to complexity, cost, performance, and consistency requirements.",
        "reference_answer": "Multi-region deployment refers to the strategy of deploying an application or service across multiple geographic regions. This can be done in an active-active or active-passive configuration.\n\nIn an active-active setup, the application runs in multiple regions simultaneously, with all regions actively serving user traffic. This provides high availability and low latency, as users can be routed to the nearest region. However, it requires more complex coordination and data replication between regions.\n\nAlternatively, an active-passive configuration has one region actively serving traffic, while the other region(s) are in standby mode, ready to take over in the event of a failover. This is simpler to implement but offers lower availability and potentially higher latency.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 5627,
        "worker_latency_ms": 19414.91449996829,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "ssh configuration security",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/ssh-configuration.md",
          "tests/fixtures/scale_docs/tls-ssl.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/firewall-rules.md",
          "tests/fixtures/scale_docs/network-troubleshooting.md"
        ],
        "worker_answer": "To improve the security of your SSH setup, you can apply the following measures:\n\n1. Use Strong Encryption: Specify strong ciphers, MACs, and key exchange algorithms in your `~/.ssh/config` file.\n2. Disable Password Authentication: Set `PasswordAuthentication no` and rely on public-key authentication by setting `PubkeyAuthentication yes`.\n3. Disable Root Login: Prevent direct root login to your SSH server by setting `PermitRootLogin no`.\n4. Limit Allowed Users: Specify a list of allowed users with the `AllowUsers` directive.\n5. Enable Logging: Increase logging verbosity to help with troubleshooting and security monitoring by setting `LogLevel INFO`.\n6. Use Trusted DNS: Specify a trusted DNS server in your SSH configuration to prevent man-in-the-middle attacks.",
        "reference_answer": "The SSH configuration file allows you to define custom settings for connecting to remote hosts, including specifying the host, user, identity file, and port. It also supports features like ProxyCommand to connect through a bastion host. SSH authentication relies on public-key cryptography, where users have a public key shared with remote servers and a private key kept secure on their local machine. To manage multiple keys, you can use the IdentityFile option in the SSH config file to specify which key to use for each host. The SSH agent is a background process that holds your private keys in memory, allowing you to authenticate with remote servers without constantly entering your passphrase.",
        "score": {
          "correctness": 1,
          "completeness": 2,
          "hallucination": 0,
          "combined": 0.4286
        },
        "tokens_in_context": 4707,
        "worker_latency_ms": 26165.099791018292,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 2, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "vpn tunneling protocols",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md"
        ],
        "worker_answer": "The context discusses several VPN tunneling protocols including IPSec, WireGuard, and OpenVPN. These protocols operate at different layers of the OSI model:\n\n- **IPSec**: Operates at Layer 3 (network layer) and uses tunnel modes to encapsulate IP packets.\n- **WireGuard**: Also operates at Layer 3 and uses modern cryptographic primitives for encryption and authentication.\n- **OpenVPN**: Operates at Layer 7 (application layer) and supports both routed (\"tunneling\") and bridged configurations.\n\nThese protocols provide encryption, authentication, and data integrity for IP traffic between two endpoints or networks.",
        "reference_answer": "VPN tunneling protocols refer to the various methods used to establish secure, encrypted connections between devices or networks over a public network like the internet. The two main VPN protocols discussed in the source text are:\n\nIPSec (Internet Protocol Security): IPSec operates at the network layer and provides encryption, authentication, and data integrity for IP traffic. It has two main modes: Transport Mode, which encrypts the payload but leaves the original IP headers intact, and Tunnel Mode, which encapsulates the entire original IP packet within a new IP packet with IPSec headers. IPSec uses the ESP and AH protocols, as well as the IKE protocol for key exchange.\n\nWireGuard: WireGuard is a newer, open-source VPN protocol that aims to be faster, simpler, and more secure than existing solutions. It operates at the network layer and uses modern cryptographic primitives like Curve25519 and ChaCha20-Poly1305. WireGuard has a minimal attack surface and uses a peer-to-peer architecture, allowing devices to communicate directly without a central VPN server.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 2158,
        "worker_latency_ms": 20783.72120799031,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "websocket protocol overview",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/websocket-protocol.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md",
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md"
        ],
        "worker_answer": "The WebSocket protocol is a computer communications protocol that provides a two-way communication channel over a single TCP connection. It was designed as an efficient alternative to traditional HTTP-based polling for real-time web applications, allowing for bidirectional communication between client and server.",
        "reference_answer": "The WebSocket protocol is a computer communications protocol that provides a two-way communication channel over a single TCP connection. It was designed as an efficient alternative to traditional HTTP-based polling for real-time web applications.\n\nThe WebSocket handshake is the process of establishing a WebSocket connection between a client and a server. The client initiates the handshake with an HTTP request that includes specific headers, and the server responds with a set of headers indicating that it is switching the protocol to WebSocket.\n\nAfter the handshake is complete, the client and server can start exchanging WebSocket frames, which are the basic units of data transmission in the WebSocket protocol. Each frame has a specific structure, including fields for the frame type, payload length, and payload data.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 5266,
        "worker_latency_ms": 19833.40008300729,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "helm charts fundamentals",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/helm-charts.md",
          "tests/fixtures/scale_docs/helm-charts.md"
        ],
        "worker_answer": "Helm charts are the packaging format for Helm, a package manager for Kubernetes. They simplify deploying and managing applications on Kubernetes by defining the necessary resources in templates, values, dependencies, hooks, tests, and repository management. Templates use Go's template language to generate Kubernetes manifest files dynamically. Values are default settings that can be overridden at install or upgrade time, and dependencies allow for linking to other charts. Hooks trigger specific actions during the release lifecycle, while testing ensures functionality post-installation or upgrade. Repository management involves storing and distributing charts through repositories accessible via Helm commands.",
        "reference_answer": "Helm charts are the unit of packaging for the Helm package manager for Kubernetes. The key fundamentals of Helm charts include:\n\nTemplates: Helm templates are written in the Go template language and used to generate Kubernetes manifest files. They can include variables, functions, and control structures for dynamic and reusable configurations.\n\nValues: The `values.yaml` file defines the default values for the chart's templates, which can be overridden when the chart is installed or upgraded.\n\nDependencies: Helm charts can depend on other Helm charts, called \"subchart\" dependencies, which are specified in the `Chart.yaml` file.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 1657,
        "worker_latency_ms": 16235.640957951546,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "linux process management",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/linux-process-management.md"
        ],
        "worker_answer": "Linux Process Management refers to the comprehensive technical documentation on managing processes in Linux, covering topics such as fork/exec operations for creating and executing new processes, process states (Running, Waiting, Stopped, Zombie), signals for inter-process communication and control, nice/renice commands for adjusting process scheduling priority, cgroups for resource allocation among processes, and namespaces for isolating system resources.",
        "reference_answer": "Linux process management involves the creation, execution, and monitoring of processes. The key aspects are:\n\n1. Fork and Exec: The `fork()` system call creates a new process (child) that is a copy of the calling process (parent). The `exec()` system call replaces the current process image with a new process image, effectively terminating the old process and starting a new one.\n\n2. Process States: Processes in Linux can be in one of several states - running, waiting, stopped, or zombie. The `ps` command can be used to view the current state of processes.\n\n3. Signals: Processes can receive and handle various signals, which are software interrupts that notify a process of an event. Signals can be used to control and manage processes.\n\n4. Nice and Renice: The `nice` and `renice` commands allow you to adjust the scheduling priority of a process, which determines how much CPU time it receives.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 2957,
        "worker_latency_ms": 22734.445042093284,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "network troubleshooting tools",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/network-troubleshooting.md",
          "tests/fixtures/scale_docs/network-troubleshooting.md",
          "tests/fixtures/scale_docs/network-troubleshooting.md",
          "tests/fixtures/scale_docs/network-troubleshooting.md"
        ],
        "worker_answer": "The document outlines several network troubleshooting tools and their common use cases:\n\n1. **tcpdump**: Captures and analyzes network traffic to identify issues like congestion, suspicious activity, or protocol-level problems.\n2. **traceroute**: Identifies the path that a packet takes from your computer to a destination IP address or hostname, useful for troubleshooting connectivity issues.\n3. **netstat**: Checks the status of network ports and associated processes.\n4. **ss**: Similar to `netstat`, but more powerful for inspecting network sockets.\n5. **dig**: Queries DNS servers and provides detailed information about DNS records, considered more powerful than `nslookup`.\n6. **nslookup**: Useful for troubleshooting DNS resolution issues, querying specific types of DNS records, and automating DNS-related tasks.\n\nThese tools are described in the context of troubleshooting network performance issues, port connectivity issues, DNS resolution problems, and general connectivity issues.",
        "reference_answer": "The key network troubleshooting tools mentioned in the source text are:\n\n1. tcpdump: A powerful network packet analysis tool that allows capturing and inspecting network traffic on a specific interface. It can be used for troubleshooting network issues, analyzing network behavior, and identifying potential security threats.\n\n2. traceroute: A network diagnostic tool that helps identify the path a packet takes from your computer to a destination IP address or hostname. It is useful for troubleshooting connectivity issues and identifying where in the network a problem might be occurring.\n\nThe text provides details on the basic usage and common use cases for each of these tools, including examples of how to use them for tasks like capturing and analyzing network traffic, troubleshooting connectivity issues, identifying security threats, and debugging application-level issues.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 6050,
        "worker_latency_ms": 28065.516083035618,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "database replication types",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/database-replication.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md",
          "tests/fixtures/scale_docs/database-replication.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md",
          "tests/fixtures/scale_docs/database-replication.md"
        ],
        "worker_answer": "Database replication involves maintaining multiple copies of a database on different servers or nodes. The main types discussed in the context are:\n\n1. **Primary-Replica (Master-Slave) Replication**: In this model, there is a single primary database that accepts all write queries. Changes are asynchronously replicated to one or more read-only replica databases.\n\n2. **Multi-Primary (Multi-Master) Replication**: This architecture allows multiple primary databases to accept write queries. Changes are replicated bidirectionally between all nodes, and a conflict resolution process is required to handle any conflicts.\n\nThese types of replication offer different trade-offs in terms of availability, consistency, and complexity.",
        "reference_answer": "Database replication can take several forms:\n\nPrimary-replica (or master-slave) replication has a single primary database that accepts all writes, and changes are asynchronously replicated to one or more read-only replica databases. This provides high availability, scalability for reads, and disaster recovery.\n\nMulti-primary (or multi-master) replication allows writes on multiple databases, with conflicts resolved automatically or manually. This supports higher write throughput but introduces more complexity around conflict resolution.\n\nSynchronous replication ensures that changes are applied atomically across all replicas, providing stronger consistency guarantees but potentially lower availability during failures.\n\nAsynchronous replication, the more common approach, allows the replicas to lag behind the primary, improving availability but potentially leading to data loss if the primary fails.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 6752,
        "worker_latency_ms": 21556.537124910392,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "nosql design patterns",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/nosql-patterns.md",
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/sample_docs/database-design.md",
          "tests/fixtures/sample_docs/database-design.md"
        ],
        "worker_answer": "NoSQL data modeling includes several key patterns and techniques such as denormalization, aggregation, time-series data modeling, graph traversal patterns, and document design principles. These patterns are designed to optimize for read performance, query efficiency, and data access patterns specific to NoSQL databases. Denormalization minimizes the need for joins by duplicating data, while aggregation groups related data into self-contained units to enable atomic updates and simplify queries. Time-series data modeling is optimized for handling high volumes of time-based measurements, allowing efficient storage and querying. Graph traversal patterns are used in graph databases to navigate relationships between entities efficiently. Document design principles focus on structuring data within a document database to match access patterns and improve performance.",
        "reference_answer": "NoSQL databases employ flexible, schema-less or schema-optional models that can accommodate unstructured, semi-structured, and rapidly changing data. Key NoSQL data modeling patterns include:\n\n1. Denormalization: NoSQL encourages denormalization to optimize for read performance and query patterns, minimizing the need for complex joins.\n2. Aggregation: NoSQL data models are structured to match common query patterns, even if it results in duplicating data across different entities.\n3. Time-series data modeling: NoSQL databases are well-suited for time-series data, allowing efficient storage and retrieval of data points over time.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 5672,
        "worker_latency_ms": 24939.428915968165,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "sql query optimization",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/sql-query-optimization.md"
        ],
        "worker_answer": "SQL query optimization involves several techniques such as using EXPLAIN plans to analyze query execution, implementing effective index strategies, rewriting queries for better performance, choosing the right join algorithms, and analyzing database statistics. These methods aim to reduce query execution time by minimizing resource usage and improving data retrieval efficiency.",
        "reference_answer": "The key techniques for SQL query optimization include:\n\n1. Using EXPLAIN plans to analyze the execution plan and identify potential performance bottlenecks, such as full table scans or suboptimal join algorithms.\n2. Selecting appropriate indexes to speed up data lookups, especially for columns used in the WHERE clause.\n3. Rewriting queries to use more efficient join algorithms, such as hash joins or merge joins, instead of nested loop joins.\n4. Analyzing database statistics to ensure the query optimizer has accurate information about the data distribution and cardinality, which can help it generate better execution plans.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 4409,
        "worker_latency_ms": 18715.164666995406,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "data pipeline design",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-pipeline-design.md"
        ],
        "worker_answer": "A data pipeline is designed to transform, integrate, and move data from various sources to a target destination like a data warehouse or analytics platform. Key components include Data Sources, Ingestion, Transformation, Loading, Orchestration, and Monitoring & Alerting. Design considerations involve handling data volume and velocity, accommodating data variety, ensuring data quality, scalability, reliability, performance, maintainability, and security. Architectural patterns include Batch Processing, Streaming Processing, Hybrid Processing, and Lambda Architecture. The choice between batch and streaming processing depends on the specific requirements of latency, fault tolerance, and real-time needs.",
        "reference_answer": "A data pipeline is a series of steps that transform, integrate, and move data from various sources to a target destination. The key components of a data pipeline include data sources, ingestion, transformation, loading, orchestration, and monitoring and alerting. When designing a data pipeline, important considerations include data volume and velocity, data variety, data quality, scalability, reliability, performance, maintainability, and security and governance. The pipeline should be designed to handle the expected data load, accommodate diverse data formats, ensure data integrity, scale as needed, and be resilient, efficient, and easy to maintain.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 7099,
        "worker_latency_ms": 22209.523499943316,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "testing strategies for software",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/testing-strategies.md",
          "tests/fixtures/scale_docs/testing-strategies.md"
        ],
        "worker_answer": "The document outlines several testing strategies crucial for ensuring the quality and reliability of software systems:\n\n1. **Unit Testing**: Focuses on verifying individual components or units of the system in isolation.\n2. **Integration Testing**: Evaluates how different parts of the system work together, including interactions and data flow between components.\n3. **End-to-End (E2E) Testing**: Validates the entire system, including user interactions and data flow through the application from a user's perspective.\n\nEach strategy serves to ensure different aspects of software quality, with unit tests checking individual parts, integration tests ensuring components work together correctly, and E2E tests confirming the system functions as expected from start to finish.",
        "reference_answer": "The primary testing strategies for software include:\n\n1. Unit Testing: Verifying the correctness of individual, isolated components or units of the system.\n2. Integration Testing: Evaluating how different components or modules of the system work together.\n3. End-to-End (E2E) Testing: Validating the entire system, including user interactions and the flow of data through the application.\n\nUnit tests focus on exercising a single, isolated component or functionality, ensuring it behaves as expected. Integration tests examine the interactions and data flow between different components or modules, simulating complete end-to-end workflows. These testing strategies play a crucial role in ensuring the overall quality and stability of the software system.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 5459,
        "worker_latency_ms": 23629.935750039294,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "dependency injection benefits",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/dependency-injection.md"
        ],
        "worker_answer": "The key benefits of using dependency injection include:\n\n1. **Testability**: Easier to write unit tests as you can substitute mock or stub implementations of dependencies.\n2. **Flexibility**: Enables swapping out implementations, enhancing maintainability and flexibility.\n3. **Modularity**: Promotes a modular design where components can be independently developed, tested, and deployed.\n4. **Inversion of Control**: Allows a central authority (IoC container) to manage object creation and lifetime, inverting control from the objects themselves.",
        "reference_answer": "Dependency injection provides several key benefits:\n\n1. Testability: It makes it easier to write unit tests by allowing you to easily substitute mock or stub implementations of dependencies.\n2. Flexibility: Decoupling objects from their dependencies enables you to easily swap out implementations, improving flexibility and maintainability.\n3. Modularity: Dependency injection promotes a modular design, as components can be developed, tested, and deployed independently.\n4. Inversion of Control: Dependency injection inverts the control of object creation and lifetime management, allowing a central authority (the IoC container) to manage these concerns.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 5015,
        "worker_latency_ms": 20941.318625002168,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "behavioral design patterns",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/design-patterns-behavioral.md",
          "tests/fixtures/scale_docs/design-patterns-behavioral.md",
          "tests/fixtures/scale_docs/design-patterns-behavioral.md",
          "tests/fixtures/scale_docs/design-patterns-behavioral.md"
        ],
        "worker_answer": "The provided context discusses several Behavioral Design Patterns including Strategy, Command, State, and possibly others like Observer, Template Method, Iterator, and Mediator. Each pattern serves different purposes:\n\n- **Strategy Pattern**: Allows runtime selection of algorithms, encapsulates them in separate classes to avoid conditional logic and make the system more flexible.\n- **Command Pattern**: Encapsulates requests as objects, allowing for parameterizing clients with actions and enabling queueing or logging of requests. It also supports undoable operations.\n- **State Pattern**: Enables an object to change its behavior based on its internal state, making it appear as if the object has changed its class.\n\nThese patterns aim to enhance flexibility and modularity in software design by encapsulating behavior within objects, allowing for dynamic changes and decoupling of components.",
        "reference_answer": "Behavioral design patterns are a category of design patterns that focus on the communication between objects and how they interact with each other. Some key behavioral design patterns include:\n\n- Observer: Allows objects to be notified of changes to the state of another object. It provides a way to implement event-driven architectures.\n- Strategy: Defines a family of algorithms, encapsulates each one, and makes them interchangeable. Clients can select the appropriate algorithm at runtime.\n- Command: Encapsulates a request as an object, allowing for the parameterization of clients with different requests, queue or log requests, and support undoable operations.\n\nThese patterns provide flexible ways to manage object interactions and communication, making the code more modular, extensible, and maintainable.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 6045,
        "worker_latency_ms": 24446.670833043754,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "creational design patterns",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/design-patterns-creational.md"
        ],
        "worker_answer": "The provided context discusses several creational design patterns including Singleton, Factory Method, Abstract Factory, Builder, and Prototype. These patterns are used to manage the creation of objects in a way that is flexible and maintainable, providing solutions for problems such as ensuring only one instance of a class exists (Singleton), decoupling object creation from usage (Factory Method, Abstract Factory), and managing complex object construction processes (Builder). Each pattern has its specific use cases and benefits, such as promoting code reusability, modularity, and ease of extension.",
        "reference_answer": "The creational design patterns are a set of design patterns that deal with object creation mechanisms, aiming to create objects in a manner suitable to the situation. The key creational design patterns are:\n\n- Singleton: Ensures a class has only one instance and provides a global point of access to it.\n- Factory Method: Defines an interface for creating an object, but lets subclasses decide which class to instantiate.\n- Abstract Factory: Provides an interface for creating families of related or dependent objects without specifying their concrete classes.\n- Builder: Separates the construction of a complex object from its representation so that the same construction process can create different representations.\n- Prototype: Specifies the kind of object to create using a prototypical instance, and creates new objects by copying this prototype.\n\nThese patterns provide different ways to create objects, allowing for flexibility, modularity, and testability in software design.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 6066,
        "worker_latency_ms": 21033.331125043333,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "how to bake sourdough in a kubernetes cluster",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 773.9295420469716,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "best brand of hiking boots for Mars",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 708.7399170268327,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "quantum entanglement in photosynthesis",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 725.0337499426678,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "the secret to eternal life according to python docs",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 718.2367079658434,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "where did I leave my keys",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 713.7660419102758,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "recipe for disaster in distributed systems",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 748.2273749774322,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "blue whales vs sperm whales",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know. The provided context does not contain information to compare blue whales and sperm whales.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 1451.3598749181256,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "history of the Roman Empire",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 717.5504580372944,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "how to play the banjo",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 719.990957994014,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "why is the sky blue",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 715.4340000124648,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "symptoms of a cold",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 706.3774999696761,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "movie recommendations for tonight",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 699.7615420259535,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "stock market predictions for 2026",
        "category": "adversarial",
        "difficulty": "hard",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 708.4080410422757,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "philosophy of mind and AI",
        "category": "adversarial",
        "difficulty": "hard",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 705.5454580113292,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "meaning of life 42",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 756.1553330160677,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "how to win friends and influence people",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [],
        "worker_answer": "I don't know. The provided context does not contain information on how to win friends and influence people.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 1469.40512501169,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "yoga for beginners",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 721.1635839194059,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "best coffee shops in London",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 719.1015419084579,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "gardening tips for summer",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 740.1917090173811,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "how to repair a flat tire",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 747.2533329855651,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "history of the internet",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 725.5407079355791,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "space travel in the future",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 706.4390419982374,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "the art of war summary",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [],
        "worker_answer": "I don't know. The provided context does not contain any information about \"The Art of War\" summary.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 1555.3854999598116,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "meditation techniques",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 692.5526659470052,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "learning a new language fast",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 734.5536249922588,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "climate change impacts",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 715.8177919918671,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "renewable energy sources",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 717.6840000320226,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "healthy eating habits",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 719.8009580606595,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "travel tips for Japan",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 719.9473329819739,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-simple",
        "query": "basics of photography",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [],
        "worker_answer": "I don't know. The provided context does not contain any information about the basics of photography.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 0,
        "worker_latency_ms": 1417.034958023578,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      }
    ],
    "candlekeep-hybrid": [
      {
        "competitor": "candlekeep-hybrid",
        "query": "What is semantic search?",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/semantic-search.md",
          "tests/fixtures/scale_docs/elasticsearch-basics.md",
          "tests/fixtures/sample_docs/document-processing.md",
          "tests/fixtures/scale_docs/data-quality.md",
          "tests/fixtures/sample_docs/vector-databases.md"
        ],
        "worker_answer": "Semantic search goes beyond keyword matching to understand the meaning and intent behind queries.",
        "reference_answer": "Semantic search is a method of information retrieval that goes beyond simple keyword matching to understand the meaning and intent behind queries. It involves techniques like query preprocessing to handle negation and expand queries with synonyms, as well as ranking and reranking approaches that consider metadata matches, recency, popularity, and other relevance signals. The goal of semantic search is to improve the accuracy and relevance of search results.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 1803,
        "worker_latency_ms": 10770.430666976608,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "vector database",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/vector-databases.md",
          "tests/fixtures/sample_docs/database-design.md",
          "tests/fixtures/sample_docs/semantic-search.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/database-replication.md"
        ],
        "worker_answer": "Vector databases store and retrieve data based on semantic similarity using embeddings, which represent documents as high-dimensional vectors. They enable operations like similarity search by calculating distances between vectors in high-dimensional space, which is useful for applications such as semantic search engines, recommendation systems, question answering systems, and document clustering/classification. Popular vector databases include ChromaDB, Pinecone, Weaviate, and Milvus.",
        "reference_answer": "A vector database is a type of database that stores and retrieves data based on semantic similarity rather than exact keyword matches. Vector databases use embeddings, which are numerical representations of text that capture semantic meaning, to represent documents as high-dimensional vectors. This allows the database to perform similarity search, where it finds documents with similar meanings by calculating distances between vectors in high-dimensional space. Common use cases for vector databases include semantic search engines, recommendation systems, question answering systems, and document clustering and classification.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 1009,
        "worker_latency_ms": 9799.847459071316,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "REST API design",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/api-design.md",
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/api-gateway-patterns.md",
          "tests/fixtures/scale_docs/api-security.md",
          "tests/fixtures/scale_docs/api-gateway-patterns.md"
        ],
        "worker_answer": "REST API design follows the principles of statelessness, resource-based URLs, standard HTTP methods and status codes. It recommends using nouns for resources in URL structure, including versioning in the URL or headers, and returning consistent error formats. Pagination is handled through query parameters like `page` and `limit`, with metadata included in the response.",
        "reference_answer": "REST API design follows several key principles:\n\n- Stateless: Each request contains all the information needed to process it, without relying on server-side state.\n- Resource-based: URLs represent resources (nouns) rather than actions (verbs).\n- HTTP methods: Standard HTTP methods like GET, POST, PUT, and DELETE are used to perform CRUD operations on resources.\n- Standard status codes: APIs return consistent HTTP status codes like 200 OK, 404 Not Found, and 500 Server Error.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 6109,
        "worker_latency_ms": 20537.209292058833,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "database normalization",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/database-design.md",
          "tests/fixtures/scale_docs/postgresql-internals.md",
          "tests/fixtures/sample_docs/vector-databases.md",
          "tests/fixtures/scale_docs/database-replication.md",
          "tests/fixtures/scale_docs/sql-query-optimization.md"
        ],
        "worker_answer": "Database normalization involves organizing the columns and tables of a database to minimize data redundancy. The provided context outlines three normal forms:\n\n1. **First Normal Form (1NF)**: Ensures atomic values in columns, unique column names, and that order doesn't matter.\n2. **Second Normal Form (2NF)**: Requires adherence to 1NF and eliminates partial dependencies, meaning all non-key columns must depend on the entire primary key.\n3. **Third Normal Form (3NF)**: Requires adherence to 2NF and eliminates transitive dependencies, ensuring non-key columns do not depend on other non-key columns.",
        "reference_answer": "Database normalization is the process of organizing data in a database to reduce redundancy and improve data integrity. The key principles of database normalization are:\n\n1. First Normal Form (1NF): Ensure that all columns contain atomic values (no arrays or lists) and each column has a unique name.\n2. Second Normal Form (2NF): Ensure that all non-key columns are fully dependent on the primary key, with no partial dependencies.\n3. Third Normal Form (3NF): Ensure that non-key columns do not have transitive dependencies, meaning they do not depend on other non-key columns.\n\nAdhering to these normalization principles helps to create a well-structured, efficient, and maintainable database schema.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 1521,
        "worker_latency_ms": 13953.218500013463,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "HTTP/2 vs HTTP/3 performance",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/http2-http3.md",
          "tests/fixtures/scale_docs/load-balancing.md",
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/owasp-top-10.md",
          "tests/fixtures/scale_docs/load-balancing.md"
        ],
        "worker_answer": "HTTP/3 aims to further improve upon the performance of HTTP/2 by using QUIC, which offers features like faster connection establishment due to UDP, improved congestion control, and reduced latency. HTTP/3 retains the multiplexing capabilities of HTTP/2 but operates over QUIC, enhancing reliability and stability.",
        "reference_answer": "HTTP/2 offers several performance improvements over HTTP/1.1, including:\n\nMultiplexing: HTTP/2 allows multiple requests and responses to be in flight simultaneously over a single TCP connection, reducing latency compared to the serial request-response model of HTTP/1.1.\n\nServer Push: HTTP/2 enables the server to proactively send resources to the client before they are requested, further improving performance by reducing round trips.\n\nHeader Compression: HTTP/2 compresses request and response headers, reducing the amount of data that needs to be transmitted.\n\nIn contrast, HTTP/3 is built on top of the QUIC protocol, which uses UDP instead of TCP. This allows for faster connection establishment and recovery from packet loss, potentially improving performance in certain network conditions compared to HTTP/2.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 5955,
        "worker_latency_ms": 20727.1201669937,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "PostgreSQL 15 internals",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/postgresql-internals.md",
          "tests/fixtures/scale_docs/linux-networking-config.md",
          "tests/fixtures/scale_docs/database-backup-recovery.md",
          "tests/fixtures/scale_docs/container-fundamentals.md",
          "tests/fixtures/scale_docs/connection-pooling.md"
        ],
        "worker_answer": "The provided context does not contain specific information about PostgreSQL 15 internals. It covers general concepts like MVCC, WAL, vacuum, query planner, indexes, and partitioning in PostgreSQL but does not specify details about version 15. Therefore, the answer is:\n\nI don't know.",
        "reference_answer": "PostgreSQL 15 uses a Multiversion Concurrency Control (MVCC) system to manage concurrent transactions and ensure data consistency. MVCC allows multiple transactions to access the same data simultaneously without causing conflicts or data corruption. PostgreSQL also uses a Write-Ahead Logging (WAL) system to ensure data durability and consistency, where changes are first recorded in the WAL before being applied to the database.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 5155,
        "worker_latency_ms": 20360.12333293911,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "Apache Kafka 3.0 features",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/apache-kafka.md",
          "tests/fixtures/scale_docs/data-pipeline-design.md",
          "tests/fixtures/scale_docs/data-lake-architecture.md",
          "tests/fixtures/scale_docs/data-lake-architecture.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about Apache Kafka 3.0 features.",
        "reference_answer": "Apache Kafka 3.0 introduced several new features:\n\n1. Improved Kafka Streams API: Kafka Streams 3.0 includes enhancements to the Kafka Streams API, such as improved support for stateful processing and new operators for data transformation and aggregation.\n\n2. Enhanced Kafka Connect: Kafka Connect 3.0 includes improvements to the Kafka Connect framework, including better support for data transformation and improved error handling.\n\n3. Expanded Kafka Security: Kafka 3.0 includes expanded security features, such as support for SASL/SCRAM authentication and improved integration with external authentication providers.\n\n4. Improved Kafka Monitoring: Kafka 3.0 includes enhancements to the Kafka monitoring and metrics system, including new metrics and improved visualization tools.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 1,
          "combined": 0.4286
        },
        "tokens_in_context": 7647,
        "worker_latency_ms": 17073.694666964002,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "TLS 1.3 handshake process",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/tls-ssl.md",
          "tests/fixtures/scale_docs/http2-http3.md",
          "tests/fixtures/scale_docs/websocket-protocol.md",
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md",
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md"
        ],
        "worker_answer": "I don't know.\n\nThe provided context does not contain information about the TLS 1.3 handshake process specifically. It details the TLS/SSL handshake up to version 1.2 and other related topics but does not cover TLS 1.3 explicitly.",
        "reference_answer": "The TLS 1.3 handshake process involves the following key steps:\n\n1. Client Hello: The client sends a message to the server that includes the TLS version, a list of supported cipher suites, compression methods, and a random value.\n\n2. Server Hello: The server responds with a message that includes the selected TLS version, cipher suite, compression method, and a random value.\n\n3. Server Certificate: The server sends its X.509 certificate chain to the client, including the server's public key certificate, any intermediate CA certificates, and the root CA certificate.\n\n4. Client Key Exchange: The client generates a pre-master secret and encrypts it with the server's public key from the certificate, then sends this encrypted pre-master secret to the server.\n\n5. Change Cipher Spec: Both the client and server send messages to indicate that all future communications will be encrypted using the negotiated cipher suite and keys.\n\n6. Finished: The client and server exchange encrypted \"Finished\" messages to confirm the successful completion of the handshake.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 8513,
        "worker_latency_ms": 21804.70774997957,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "Kubernetes v1.24 removal of dockershim",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/kubernetes-architecture.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/container-fundamentals.md",
          "tests/fixtures/scale_docs/helm-charts.md",
          "tests/fixtures/scale_docs/container-security.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about the removal of dockershim in Kubernetes v1.24.",
        "reference_answer": "Kubernetes v1.24 will remove the dockershim, which was a component that provided compatibility between Kubernetes and the Docker container runtime. With this removal, Kubernetes will no longer support the Docker runtime directly, and users will need to use alternative container runtimes like containerd or CRI-O. This change is part of Kubernetes' effort to decouple the container runtime from the core Kubernetes components, allowing for more flexibility and innovation in the container runtime ecosystem.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 2328,
        "worker_latency_ms": 15478.815332986414,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "gRPC over HTTP/2",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/http2-http3.md",
          "tests/fixtures/scale_docs/grpc-protocol.md",
          "tests/fixtures/sample_docs/microservices.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about gRPC over HTTP/2.",
        "reference_answer": "gRPC is a modern open-source remote procedure call (RPC) framework that uses HTTP/2 as the underlying transport protocol. The key features of gRPC over HTTP/2 are:\n\n1. Efficient binary serialization using Protocol Buffers (protobuf) for data exchange.\n2. Bidirectional streaming support, allowing for efficient real-time communication between clients and servers.\n3. Automatic generation of client and server stubs from protobuf service definitions, simplifying application development.\n4. Support for deadlines and cancellation, enabling robust and responsive distributed systems.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 8487,
        "worker_latency_ms": 1765.7903330400586,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "Redis 7.0 Multi-part AOF",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/redis-data-structures.md",
          "tests/fixtures/scale_docs/helm-charts.md",
          "tests/fixtures/scale_docs/redis-data-structures.md",
          "tests/fixtures/scale_docs/apache-kafka.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "Redis 7.0 introduced a new feature called \"Multi-part AOF\", which enhances the Append-Only File (AOF) persistence model. The Multi-part AOF allows the AOF file to be split into multiple files, improving the performance and reliability of the AOF persistence. This feature enables more efficient handling of large AOF files, as the Redis server can load and rewrite the AOF in a more scalable manner.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 3224,
        "worker_latency_ms": 17925.99458305631,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "Elasticsearch 8.x security changes",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/elasticsearch-basics.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/scale_docs/elasticsearch-basics.md",
          "tests/fixtures/scale_docs/linux-logging.md",
          "tests/fixtures/scale_docs/elasticsearch-basics.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "Elasticsearch 8.x introduced several security changes:\n\n1. Mandatory TLS encryption: Elasticsearch 8.x requires TLS encryption for all HTTP and transport connections by default. This enhances the security of data transmission.\n\n2. Removal of basic authentication: Elasticsearch 8.x no longer supports basic authentication. Users must now authenticate using token-based or PKI-based authentication methods.\n\n3. Deprecation of the `elasticsearch-users` file: The `elasticsearch-users` file for managing user credentials has been deprecated in favor of the new Elasticsearch security plugin.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 1523,
        "worker_latency_ms": 9726.795958005823,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "OWASP Top 10 2021",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/owasp-top-10.md",
          "tests/fixtures/scale_docs/elasticsearch-basics.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/scale_docs/owasp-top-10.md"
        ],
        "worker_answer": "I don't know. The provided context does not specify the OWASP Top 10 for the year 2021. It only mentions a guide on prevention techniques for OWASP vulnerabilities but does not list the specific top 10 vulnerabilities for any year.",
        "reference_answer": "The OWASP Top 10 2021 is a list of the most critical security risks to web applications, as identified by the Open Web Application Security Project (OWASP). The top vulnerabilities include injection flaws (such as SQL injection and OS command injection), broken authentication, cross-site scripting (XSS), security misconfiguration, and insecure deserialization. To prevent these vulnerabilities, the document recommends techniques like input validation, using parameterized queries, applying the principle of least privilege, and leveraging web application firewalls and dynamic query builders.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 1,
          "combined": 0.4286
        },
        "tokens_in_context": 947,
        "worker_latency_ms": 8446.615332970396,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "AVX-512 vector instructions",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/vector-databases.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/design-patterns-behavioral.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 0,
          "combined": 0.0
        },
        "tokens_in_context": 3379,
        "worker_latency_ms": 17425.449250033125,
        "judge_raw_response": "no reference answer"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "TCP/IP layer 4",
        "category": "lexical",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md",
          "tests/fixtures/scale_docs/load-balancing.md",
          "tests/fixtures/scale_docs/firewall-rules.md",
          "tests/fixtures/scale_docs/load-balancing.md",
          "tests/fixtures/scale_docs/http2-http3.md"
        ],
        "worker_answer": "Transport Layer",
        "reference_answer": "The Transport Layer in the TCP/IP protocol stack corresponds to layer 4 of the OSI model. This layer is responsible for ensuring complete data transfer between applications, including segmentation, flow control, and error correction. The two main protocols at the Transport Layer are TCP (Transmission Control Protocol) and UDP (User Datagram Protocol). TCP provides reliable, connection-oriented data transfer, while UDP offers unreliable, connectionless data transfer.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 8462,
        "worker_latency_ms": 16672.358624986373,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "IPv6 address format",
        "category": "lexical",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/dns-resolution.md",
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md",
          "tests/fixtures/scale_docs/cloud-networking.md",
          "tests/fixtures/scale_docs/network-troubleshooting.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md"
        ],
        "worker_answer": "IPv6 addresses are represented in eight groups of four hexadecimal digits, separated by colons. For example: `2606:2800:220:1:248:1893:25c8:1946`.",
        "reference_answer": "An IPv6 address is a 128-bit address used to identify devices on an IPv6 network. The format of an IPv6 address consists of eight groups of four hexadecimal digits separated by colons, such as `2001:0db8:85a3:0000:0000:8a2e:0370:7334`. The first 64 bits represent the network prefix, and the last 64 bits represent the interface identifier, which is typically derived from the device's MAC address.",
        "score": {
          "correctness": 2,
          "completeness": 1,
          "hallucination": 1,
          "combined": 0.5714
        },
        "tokens_in_context": 3323,
        "worker_latency_ms": 21478.022333001718,
        "judge_raw_response": "{\"correctness\": 2, \"completeness\": 1, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "UUID v4 generation",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/testing-strategies.md",
          "tests/fixtures/scale_docs/linux-filesystem.md",
          "tests/fixtures/scale_docs/cryptography-basics.md",
          "tests/fixtures/sample_docs/database-design.md",
          "tests/fixtures/scale_docs/container-security.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about UUID v4 generation.",
        "reference_answer": "UUID v4 is a type of Universally Unique Identifier (UUID) that is randomly generated. The UUID v4 specification defines a method for generating UUIDs from random or pseudo-random numbers. This method produces a 128-bit number that is very likely to be unique across space and time. UUIDs generated using this method do not require a central registration authority and can be generated without consultation with that authority.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 2520,
        "worker_latency_ms": 16223.535374971107,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "0xEF hex code",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/api-security.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/ci-cd-pipelines.md",
          "tests/fixtures/scale_docs/error-handling-patterns.md",
          "tests/fixtures/scale_docs/websocket-protocol.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The hexadecimal code 0xEF does not have a specific meaning in the context of cryptography. Hexadecimal codes are used to represent binary data, but 0xEF is not a commonly used value in cryptographic algorithms or standards. The source text does not mention the hexadecimal code 0xEF or provide any information about its meaning or usage in cryptography.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 2918,
        "worker_latency_ms": 18230.874457978643,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "Python 3.10 match statement",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/elasticsearch-basics.md",
          "tests/fixtures/scale_docs/functional-programming.md",
          "tests/fixtures/scale_docs/owasp-top-10.md",
          "tests/fixtures/scale_docs/shell-scripting.md",
          "tests/fixtures/scale_docs/data-serialization.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The Python 3.10 match statement is a new pattern matching feature introduced in Python 3.10. It allows you to match a value against a series of patterns, similar to a switch statement in other programming languages. The match statement provides a concise and expressive way to handle complex data structures and control flow in your Python code.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 1399,
        "worker_latency_ms": 9968.292875098996,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "ISO 27001 compliance",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/compliance-frameworks.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md"
        ],
        "worker_answer": "ISO 27001 is an information security management system (ISMS) standard that provides a framework for establishing, implementing, maintaining, and continually improving an organization's information security management system. Compliance with ISO 27001 involves understanding the organization and its context, demonstrating leadership commitment to the ISMS, conducting risk assessments, planning actions to address risks and opportunities, providing necessary resources for support, implementing operational controls, evaluating performance of the ISMS, and continuously improving it. To become ISO 27001 certified, organizations must establish an ISMS based on the standard's requirements, conduct a comprehensive risk assessment and implement controls to mitigate risks, undergo an external audit by an accredited certification body, and maintain the ISMS for annual certification renewal.",
        "reference_answer": "ISO 27001 is an international standard that specifies the requirements for an information security management system (ISMS). To achieve ISO 27001 compliance, organizations must implement a comprehensive set of security controls across 14 domains, including information security policies, access control, cryptography, physical security, operations security, and incident management. The standard requires organizations to conduct regular risk assessments, implement appropriate security measures, and undergo third-party audits to verify the effectiveness of their ISMS.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 6682,
        "worker_latency_ms": 24974.26229098346,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "SOC2 Type II audit",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/compliance-frameworks.md",
          "tests/fixtures/scale_docs/compliance-frameworks.md",
          "tests/fixtures/scale_docs/compliance-frameworks.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "A SOC 2 Type II audit evaluates the design and operating effectiveness of an organization's controls related to security, availability, processing integrity, confidentiality, and privacy of data over a period of time, typically 6-12 months. To achieve SOC 2 compliance, organizations must implement controls across these 5 trust principles, including access management, logging and monitoring, change management, vulnerability management, incident response, backup and disaster recovery, risk assessment, vendor management, and security awareness training.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 0,
          "combined": 0.0
        },
        "tokens_in_context": 9817,
        "worker_latency_ms": 15794.113083044067,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "BGE-small-en-v1.5 model",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-quality.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/design-patterns-creational.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/cloud-storage-options.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The bge-small-en-v1.5 model is the embedding model used by the Candlekeep RAG (Retrieval-Augmented Generation) knowledge base server. Candlekeep uses this model as part of its \"Arcane Recall\" component within the RAG pipeline, which is responsible for the retrieval of relevant information from the underlying document database.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 2343,
        "worker_latency_ms": 14218.587332987227,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "ChromaDB v0.4.0",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/vector-databases.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/container-security.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about ChromaDB v0.4.0. It only mentions that ChromaDB is an open-source, Python-native vector database but does not provide details about specific versions.",
        "reference_answer": "ChromaDB v0.4.0 is not mentioned in the given source document. The document discusses the design of the Candlekeep system, which provides a RAG knowledge base that an AI agent can query. It describes the design goals and decisions, such as having three search paths (simple, hybrid, and precise), having the agent decompose queries rather than the search tool, and using \"Arcane Recall\" to return adjacent chunks around each match. However, there is no information about the specific ChromaDB version 0.4.0.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 3015,
        "worker_latency_ms": 20832.126958062872,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "MS-MARCO MiniLM L-6-v2",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/scale_docs/data-warehouse-modeling.md",
          "tests/fixtures/scale_docs/container-security.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain any information related to \"MS-MARCO MiniLM L-6-v2\". The context is about system calls and ETL patterns in data warehousing.",
        "reference_answer": "The MS-MARCO MiniLM L-6-v2 is a technique that is part of the \"Recommended Stack\" for the RAG (Retrieval Augmented Generation) system optimization project described in the source document. Specifically, the document states that the \"Tier 1 (Always Use)\" recommendation is to use the \"Bardic Knowledge\" and \"Arcane Recall\" techniques, which together achieve a precision of 97.3%, content quality of 88.7%, and latency of 37ms. The \"Tier 2 (Conditional)\" recommendation is to use the \"Flurry of Blows\" technique for complex queries, if the additional latency of around 1100ms is acceptable.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 3564,
        "worker_latency_ms": 19045.723834075034,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "JWT RS256 signing",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/authentication.md",
          "tests/fixtures/scale_docs/cryptography-basics.md",
          "tests/fixtures/scale_docs/api-gateway-patterns.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/api-security.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about JWT RS256 signing. It mainly covers token-based authentication with JWT, ECDSA key generation and verification in Python, API gateway patterns including JWT validation, OAuth 2.0 flows, PKCE, token introspection, API keys, HMAC signing, and request signing but does not specify RS256 signing details.",
        "reference_answer": "JWT (JSON Web Tokens) use the RS256 algorithm for signing. This is an asymmetric cryptographic algorithm that uses a private key to sign the token and a public key to verify it. The JWT structure consists of a header, payload, and signature. The header specifies the algorithm used, the payload contains the claims, and the signature is generated using the private key to ensure the integrity of the token. This allows for stateless authentication, where the server does not need to store session information.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 5820,
        "worker_latency_ms": 22220.305083086714,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "OAuth 2.0 PKCE flow",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/api-security.md",
          "tests/fixtures/scale_docs/api-gateway-patterns.md",
          "tests/fixtures/sample_docs/authentication.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md"
        ],
        "worker_answer": "I don't know.\n\nThe provided context does not contain information about the OAuth 2.0 PKCE (Proof Key for Code Exchange) flow. It only mentions that the guide will cover PKCE but does not provide details about it.",
        "reference_answer": "The OAuth 2.0 PKCE (Proof Key for Code Exchange) flow is a security enhancement to the standard OAuth 2.0 authorization code grant flow. It is designed to protect against authorization code interception attacks, particularly in public clients like mobile apps or single-page applications (SPAs).\n\nIn the PKCE flow, the client application generates a cryptographically random \"code verifier\" value and derives a \"code challenge\" from it. The client sends the code challenge to the authorization server when requesting an authorization code. When the client later exchanges the authorization code for an access token, it also sends the original code verifier. The authorization server verifies that the code verifier matches the previously sent code challenge, ensuring that the token exchange is performed by the same client that initiated the authorization request.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 7071,
        "worker_latency_ms": 19740.988582954742,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "RAID 6 configurations",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/network-troubleshooting.md",
          "tests/fixtures/scale_docs/disaster-recovery.md",
          "tests/fixtures/scale_docs/cloud-networking.md",
          "tests/fixtures/scale_docs/linux-filesystem.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "RAID 6 configurations provide data redundancy by using two independent parity blocks. This allows the system to continue operating even if two disks fail simultaneously. RAID 6 can tolerate the loss of any two disks in the array without data loss, making it a robust storage solution for mission-critical applications.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 3796,
        "worker_latency_ms": 18586.9244170608,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "SQL-92 standards",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-quality.md",
          "tests/fixtures/scale_docs/data-pipeline-design.md",
          "tests/fixtures/scale_docs/compliance-frameworks.md",
          "tests/fixtures/scale_docs/sql-query-optimization.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about SQL-92 standards.",
        "reference_answer": "SQL-92 refers to the SQL standard published in 1992, which was the third major revision of the SQL language. This standard introduced several key features, including:\n\n- Support for outer joins, which allow for the inclusion of rows that do not have matching values in the joined tables.\n- Improved support for subqueries, including the ability to use subqueries in the SELECT, FROM, and WHERE clauses.\n- Standardized syntax for common SQL operations, such as CREATE TABLE, ALTER TABLE, and DROP TABLE.\n- Improved support for data types, including the introduction of the TIMESTAMP data type.\n\nThe SQL-92 standard laid the foundation for the modern SQL language and is still widely used today, even as newer versions of the standard have been published.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 5508,
        "worker_latency_ms": 17455.736791947857,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "POSIX thread management",
        "category": "lexical",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/concurrency-patterns.md",
          "tests/fixtures/scale_docs/linux-performance-tuning.md",
          "tests/fixtures/scale_docs/concurrency-patterns.md",
          "tests/fixtures/scale_docs/linux-performance-tuning.md",
          "tests/fixtures/scale_docs/concurrency-patterns.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about POSIX thread management. It discusses general concepts of concurrency patterns and constructs, focusing on thread pools but does not delve into POSIX-specific details.",
        "reference_answer": "POSIX thread management in Linux is handled through the use of the `fork()` and `exec()` system calls. The `fork()` system call creates a new process, which is a copy of the calling process, and the `exec()` system call replaces the current process image with a new process image. Processes in Linux can be in one of several states, including running, waiting, stopped, and zombie. The current state of processes can be viewed using the `ps` command.",
        "score": {
          "correctness": 0,
          "completeness": 1,
          "hallucination": 1,
          "combined": 0.2857
        },
        "tokens_in_context": 8369,
        "worker_latency_ms": 18425.33416603692,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 1, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "systemd unit files",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/systemd-services.md",
          "tests/fixtures/scale_docs/linux-filesystem.md"
        ],
        "worker_answer": "Systemd uses \"unit files\" to manage services, sockets, mounts, devices, and other system resources. Unit files are configuration files that define how a particular service or resource should be managed. They typically have extensions like `.service`, `.socket`, `.target`, `.timer`, etc., and are usually located in `/etc/systemd/system/` or `/usr/lib/systemd/system/`.",
        "reference_answer": "Systemd unit files are configuration files used by the systemd init system to manage services, sockets, mounts, devices, and other system resources on Linux. Unit files are typically located in the /etc/systemd/system/ and /usr/lib/systemd/system/ directories, and have extensions like .service, .socket, .target, or .timer depending on the type of unit.\n\nUnit files define how a particular service or resource should be managed, including the command to start/stop the service, dependencies on other units, and whether the service should be automatically restarted. Systemd also uses \"targets\" to group related services and resources together, similar to the runlevels used in older init systems.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 5363,
        "worker_latency_ms": 22033.382958034053,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "EXT4 filesystem limits",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/linux-filesystem.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/owasp-top-10.md",
          "tests/fixtures/scale_docs/systemd-services.md",
          "tests/fixtures/scale_docs/linux-performance-tuning.md"
        ],
        "worker_answer": "The maximum number of inodes is set during filesystem creation and cannot be changed later.",
        "reference_answer": "The ext4 filesystem has the following limits:\n\n- Maximum file size: 16TB\n- Maximum filesystem size: 1EB (exabyte)\n- Maximum number of subdirectories within a directory: 64,000\n- Maximum number of hard links to a file: 65,000\n\nThese limits are defined by the ext4 filesystem design and cannot be changed after the filesystem is created.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 7376,
        "worker_latency_ms": 19192.13437498547,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "YAML 1.2 syntax",
        "category": "lexical",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/configuration-management.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/container-fundamentals.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/data-serialization.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "YAML 1.2 syntax is the version of the YAML data serialization format used by Ansible. Ansible Playbooks, which define the configuration management tasks to be executed, are written in YAML 1.2 format. The YAML syntax used in Ansible Playbooks follows a specific structure, including sections for hosts, tasks, handlers, variables, and templates. Ansible Playbooks are executed using the `ansible-playbook` command.",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 1,
          "combined": 0.1429
        },
        "tokens_in_context": 1171,
        "worker_latency_ms": 7514.9138330016285,
        "judge_raw_response": "{\"correctness\": 0, \"completeness\": 0, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "Docker Compose v2",
        "category": "lexical",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/container-fundamentals.md",
          "tests/fixtures/scale_docs/ci-cd-pipelines.md",
          "tests/fixtures/sample_docs/api-design.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/container-fundamentals.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about Docker Compose v2. It only describes how to use Docker Compose for defining and running multi-container Docker applications with an example `docker-compose.yml` file.",
        "reference_answer": "Docker Compose v2 is a newer version of the Docker Compose tool, which is used to define and run multi-container Docker applications. The key components that enable container technology, as described in the source text, are namespaces, control groups (cgroups), and union file systems (OverlayFS). These Linux kernel features provide isolation, resource management, and efficient layering of file systems for containers.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 5149,
        "worker_latency_ms": 20104.096083086915,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "AWS IAM policies",
        "category": "lexical",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/secrets-management.md",
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/data-lake-architecture.md",
          "tests/fixtures/scale_docs/cost-optimization.md",
          "tests/fixtures/scale_docs/secrets-management.md"
        ],
        "worker_answer": "IAM policies can be used to grant or deny access to specific secrets in AWS Secrets Manager. For example, an IAM policy can allow read access to a particular secret using actions like `secretsmanager:GetSecretValue` and specifying the ARN of the secret.",
        "reference_answer": "AWS IAM (Identity and Access Management) policies are JSON-based documents that define the permissions for AWS resources. These policies specify the actions that are allowed or denied on specific resources, such as Amazon S3 buckets, Amazon EC2 instances, or AWS Lambda functions. IAM policies can be attached to IAM users, groups, or roles to control their access to AWS services and resources. They are a fundamental component of AWS security and access management, allowing organizations to implement the principle of least privilege and grant only the necessary permissions to their AWS entities.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 6010,
        "worker_latency_ms": 19948.453832999803,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "how to implement chaos engineering",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/chaos-engineering.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md"
        ],
        "worker_answer": "To implement chaos engineering, follow these steps based on the provided context:\n\n1. Define a hypothesis about your system's behavior under failure conditions.\n2. Run experiments continuously, not just during initial development or before major releases.\n3. Ensure experiments are conducted in environments that closely mimic production conditions.\n4. Gradually increase the scale and impact of experiments as confidence in system resilience grows.\n5. Use fault injection techniques such as network latency, resource exhaustion, service failures, dependency failures, and Chaos Monkey.\n6. Conduct blameless postmortems to analyze experiment outcomes and identify areas for improvement.\n7. Schedule regular game days involving cross-functional teams to test resilience and foster a culture of continuous improvement.\n8. Define clear steady state hypotheses that specify expected system behavior under normal conditions and verify against these during experiments.\n9. Carefully manage the blast radius of each experiment to control its impact and avoid unintended disruptions.\n\nThese steps are based on the principles outlined in the provided document about chaos engineering.",
        "reference_answer": "To implement chaos engineering, you can follow these key principles and techniques:\n\n1. Conduct hypothesis-driven experiments to test the system's resilience under turbulent conditions. Clearly define a testable hypothesis about the system's behavior.\n\n2. Run chaos experiments continuously, not just during initial development or before major releases. Resilience must be an ongoing concern.\n\n3. Use controlled environments that closely mimic production to ensure representative results. Start with small, low-impact experiments and gradually increase the blast radius.\n\n4. Inject faults like network latency, resource exhaustion, service failures, and dependency failures to observe how the system responds. Tools like the Chaos Toolkit can help automate these fault injection experiments.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 5793,
        "worker_latency_ms": 26497.83074995503,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "benefits of event-driven architecture",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/data-warehouse-modeling.md",
          "tests/fixtures/scale_docs/data-warehouse-modeling.md"
        ],
        "worker_answer": "The benefits of event-driven architecture include:\n\n1. **Loose Coupling**: Components are decoupled from each other, allowing for easier maintenance, independent scaling, and more flexibility.\n2. **Asynchronous Communication**: Events can be processed asynchronously, improving the overall responsiveness and scalability of the system.\n3. **Scalability**: Event-driven architectures can scale individual components independently, as they are decoupled from each other.\n4. **Fault Tolerance**: If one component fails, the rest of the system can continue to function, improving the overall resilience of the system.\n5. **Audit Trail**: The event store provides a comprehensive audit trail of all the actions and state changes that have occurred in the system.",
        "reference_answer": "The key benefits of event-driven architecture include:\n\n1. Loose coupling: Components are decoupled, allowing for easier maintenance, independent scaling, and more flexibility.\n2. Asynchronous communication: Events can be processed asynchronously, improving responsiveness and scalability.\n3. Scalability: Individual components can scale independently due to the decoupled nature of the architecture.\n4. Fault tolerance: If one component fails, the rest of the system can continue to function, improving overall resilience.\n5. Audit trail: The event store provides a comprehensive record of all actions and state changes in the system.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 8058,
        "worker_latency_ms": 23887.489708024077,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "serverless vs containers",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/container-fundamentals.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/container-fundamentals.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain a direct comparison between serverless and containers. It focuses on AWS Lambda, cold starts, event sources, Step Functions, and cost optimization within the context of serverless architecture.",
        "reference_answer": "Serverless architecture and containers are two different approaches to cloud computing. Serverless architecture, exemplified by AWS Lambda, is a cloud computing execution model where the cloud provider is responsible for executing code and managing the underlying infrastructure. In this model, developers focus on writing code without worrying about provisioning or managing servers. Containers, on the other hand, are a way to package and run applications in a consistent, isolated environment, allowing for portability and scalability across different computing environments.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 8746,
        "worker_latency_ms": 18450.752416043542,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "microservices data consistency",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/nosql-patterns.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md",
          "tests/fixtures/scale_docs/data-warehouse-modeling.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md"
        ],
        "worker_answer": "Data consistency in microservices can be managed using patterns like the Saga Pattern and Event Sourcing. The Saga Pattern involves a series of local transactions across services, with each step publishing an event and having potential compensation actions for failure. Event Sourcing involves storing events to rebuild state, providing an audit trail and handling consistency issues through replaying these events.",
        "reference_answer": "",
        "score": {
          "correctness": 0,
          "completeness": 0,
          "hallucination": 0,
          "combined": 0.0
        },
        "tokens_in_context": 1896,
        "worker_latency_ms": 13035.338583984412,
        "judge_raw_response": "no reference answer"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "data lakehouse architecture",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-lake-architecture.md",
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/apache-kafka.md",
          "tests/fixtures/sample_docs/microservices.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain specific information about data lakehouse architecture, although it mentions \"lakehouse patterns\" in the description. For a detailed explanation of data lakehouse architecture, additional context or information would be required.",
        "reference_answer": "The data lakehouse architecture combines the flexibility and scalability of a data lake with the structured data management capabilities of a data warehouse. Key components include:\n\n1. Ingestion Layer: Responsible for bringing data into the data lake from various sources, using tools like Apache Kafka, Apache Spark Streaming, AWS Kinesis, and Azure Event Hubs.\n\n2. Storage Layer: The foundation of the data lake, where raw, unprocessed data is stored using object storage services like Amazon S3, Azure Blob Storage, or Google Cloud Storage.\n\n3. Processing Layer: Responsible for transforming and enriching the raw data stored in the data lake, using tools like Apache Spark, Apache Flink, AWS Glue, or Azure Databricks for batch and stream processing.",
        "score": {
          "correctness": 1,
          "completeness": 1,
          "hallucination": 0,
          "combined": 0.2857
        },
        "tokens_in_context": 6173,
        "worker_latency_ms": 21924.041499965824,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 1, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "zero trust security principles",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/compliance-frameworks.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md"
        ],
        "worker_answer": "The core principles of zero trust architecture include:\n\n1. Verify Explicitly: All users, devices, and applications must be continuously authenticated and authorized.\n2. Use Least Privilege Access: Define granular access policies to grant the minimum necessary privileges.\n3. Assume Breach: Design security controls focusing on data protection and rapid response.\n4. Verify and Monitor Everywhere: Continuously monitor all network traffic, user activities, and system events for validation.",
        "reference_answer": "The core principles of zero trust security include:\n\n1. Verify Explicitly: All users, devices, and applications must be continuously authenticated and authorized, regardless of network location.\n2. Use Least Privilege Access: Granular access policies should be defined to grant the minimum necessary privileges for each user, device, or application to perform their required functions.\n3. Assume Breach: Organizations should assume that a breach has already occurred and design security controls accordingly, focusing on data protection and rapid response.\n4. Verify and Monitor Everywhere: All network traffic, user activities, and system events should be continuously monitored and validated, both on-premises and in the cloud.\n\nImplementing a zero trust architecture involves key components such as identity verification and access control, micro-segmentation and least privilege, and continuous validation and monitoring.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 1552,
        "worker_latency_ms": 12578.407332999632,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "continuous integration best practices",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/testing-strategies.md",
          "tests/fixtures/sample_docs/api-design.md",
          "tests/fixtures/scale_docs/data-pipeline-design.md",
          "tests/fixtures/sample_docs/microservices.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about continuous integration best practices.",
        "reference_answer": "The key continuous integration best practices based on the source text are:\n\n1. Establish a clear CI/CD pipeline with well-defined stages, including source code management, build, test, static code analysis, artifact storage, and deployment.\n2. Automate the pipeline stages using tools like Jenkins, with specific steps for compiling the source code, running tests, analyzing the code, storing artifacts, and deploying the application.\n3. Manage the generated artifacts, such as compiled binaries, Docker images, and test reports, by storing them in a repository for later use.\n4. Implement parallel execution of pipeline stages to improve efficiency and reduce the overall build time.\n5. Leverage caching mechanisms to speed up the pipeline by reusing previously built artifacts or test results.\n6. Adopt deployment strategies like blue-green, canary, or rolling deployments to ensure reliable and safe application updates.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 2818,
        "worker_latency_ms": 15652.491834014654,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "database sharding strategies",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/database-sharding.md",
          "tests/fixtures/scale_docs/elasticsearch-basics.md"
        ],
        "worker_answer": "Database sharding strategies include:\n\n1. Hash-based sharding - Uses a hash function to determine the shard for data items, ensuring even distribution across shards but potentially leading to uneven distribution if data is not uniformly distributed. Pros include load balancing and transparent application treatment of the database, while cons involve complexity in cross-shard queries and challenges in resharding.\n\n2. Range-based sharding - Partitions data based on ranges of values, suitable for naturally divisible data like dates or geographic regions. Pros include intuitive management and efficient cross-shard queries, but cons involve potential for uneven data distribution and hotspots.\n\n3. Directory-based sharding - Uses a centralized directory to map data items to specific shards, offering flexibility and easier resharding but introducing additional complexity and potential for a bottleneck.\n\nCross-shard queries can be handled through scatter-gather, routed queries, or materialized views. Resharding strategies include shard splitting, merging, and rebalancing to manage changes in data or workload.",
        "reference_answer": "Database sharding strategies refer to the different approaches used to horizontally partition data across multiple database instances, or \"shards\", to improve scalability and performance. The three main sharding strategies are:\n\n1. Hash-based sharding: This uses a hash function to determine the shard where a particular data item is stored, based on a specific column or set of columns. It results in even data distribution but can make cross-shard queries more complex.\n\n2. Range-based sharding: This partitions data based on a range of values in a specific column or set of columns, often used when data can be naturally divided into logical ranges. It can enable more efficient cross-shard queries but may lead to uneven data distribution.\n\n3. Directory-based sharding: This uses a central directory to map data to specific shards, allowing more flexibility in shard management but introducing additional complexity and a potential single point of failure.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 3757,
        "worker_latency_ms": 27393.223291961476,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "load balancing algorithms",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/load-balancing.md",
          "tests/fixtures/scale_docs/database-sharding.md",
          "tests/fixtures/scale_docs/websocket-protocol.md",
          "tests/fixtures/scale_docs/cdn-architecture.md",
          "tests/fixtures/scale_docs/well-architected-framework.md"
        ],
        "worker_answer": "Load balancing algorithms are the core logic that determines how incoming traffic is distributed across a pool of backend servers. The most common load balancing algorithms include:\n\n- Round-robin: Sends each new connection to the next server in the list, cycling through the entire pool.\n- Least connections: Sends each new connection to the server with the fewest number of active connections.\n- Weighted: Allows assigning a weight value to each backend server, directing more traffic to servers with higher weights.\n- IP hash: Uses a hash of the client's IP address to determine which server the connection should be sent to, ensuring that a client is always sent to the same backend server.",
        "reference_answer": "The most common load balancing algorithms are:\n\nRound-robin: Sends each new connection to the next server in the list, cycling through the entire pool to ensure each server receives an equal number of connections.\n\nLeast connections: Sends each new connection to the server with the fewest number of active connections, helping to balance the load more evenly across backend servers.\n\nWeighted: Allows assigning a weight value to each backend server, so servers with a higher weight receive more traffic than those with a lower weight, useful for servers with different capacities.\n\nIP hash: Uses a hash of the client's IP address to determine which server the connection should be sent to, maintaining session affinity.\n\nLoad balancers can operate at different layers of the OSI model, with layer 4 load balancers making decisions based on transport layer information like IP addresses and ports, while layer 7 load balancers have more visibility into the application layer.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 5589,
        "worker_latency_ms": 26373.810459044762,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "content delivery network optimization",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cdn-architecture.md",
          "tests/fixtures/scale_docs/load-balancing.md",
          "tests/fixtures/scale_docs/concurrency-patterns.md",
          "tests/fixtures/scale_docs/cdn-architecture.md",
          "tests/fixtures/scale_docs/network-security-hardening.md"
        ],
        "worker_answer": "Content Delivery Network (CDN) optimization involves several key strategies such as edge caching, geographic routing, and anycast. These techniques aim to reduce latency, improve reliability, and enhance the overall user experience by efficiently delivering content from servers closest to the end-users. Additionally, optimizing CDNs includes managing cache invalidation and origin shielding to ensure content is up-to-date and secure.",
        "reference_answer": "A Content Delivery Network (CDN) is designed to efficiently deliver content to users around the world. The key components of a CDN architecture include edge servers, which are strategically placed to minimize the distance and latency between the user and the content, and an origin server, which is the primary source of the content. Load balancers direct user requests to the appropriate edge server, and a cache management system manages the content stored on the edge servers. Edge caching is a fundamental aspect of CDN architecture, where content is stored on the edge servers closest to the end-users, allowing the CDN to serve content directly from the edge and reduce latency and load on the origin server.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 2368,
        "worker_latency_ms": 17622.553750057705,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "service mesh advantages",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/service-mesh.md",
          "tests/fixtures/scale_docs/migration-strategies.md",
          "tests/fixtures/scale_docs/service-mesh.md"
        ],
        "worker_answer": "The advantages of a service mesh include:\n\n- Handling cross-cutting concerns like traffic management, security, and observability.\n- Decoupling application code from infrastructure concerns, allowing developers to focus on business logic.\n- Providing advanced traffic control features like load balancing and circuit breaking.\n- Enabling secure communication between services using mTLS for authentication and encryption.\n- Collecting telemetry data to provide visibility into the health and performance of services.",
        "reference_answer": "The key advantages of a service mesh architecture include:\n\n1. Traffic management: A service mesh provides advanced traffic control features such as load balancing, circuit breaking, retries, and timeouts.\n\n2. Security: The service mesh can enable secure communication between services using mutual TLS (mTLS) for authentication and encryption.\n\n3. Observability: The service mesh collects telemetry data, including metrics, logs, and traces, to provide visibility into the health and performance of the mesh.\n\n4. Decoupling of application and infrastructure concerns: The service mesh architecture separates the application code from the infrastructure concerns, allowing developers to focus on building business logic while the service mesh handles cross-cutting concerns.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 6113,
        "worker_latency_ms": 22914.694166043773,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "infrastructure as code tools",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/infrastructure-as-code.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md"
        ],
        "worker_answer": "The provided context specifically discusses Terraform, an Infrastructure as Code (IaC) tool developed by HashiCorp. It allows for the definition and provisioning of infrastructure in a declarative manner, making it easier to manage, collaborate on, and version control infrastructure. Terraform supports a wide range of cloud providers and on-premises resources, enabling users to manage their infrastructure as code effectively.",
        "reference_answer": "Terraform is an open-source Infrastructure as Code (IaC) tool that allows you to define and provision your infrastructure in a declarative manner. It supports a wide range of cloud providers and on-premises resources. Terraform maintains the state of your infrastructure in a state file, which is typically stored in a backend, such as a local file, an S3 bucket, or a HashiCorp Consul backend. Terraform supports state locking to prevent multiple users or processes from modifying the state file at the same time, and state importing to add existing resources to the Terraform state.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 9404,
        "worker_latency_ms": 20937.98849999439,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "monitoring vs observability",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/monitoring-observability.md",
          "tests/fixtures/scale_docs/service-mesh.md",
          "tests/fixtures/scale_docs/data-pipeline-design.md",
          "tests/fixtures/scale_docs/service-mesh.md"
        ],
        "worker_answer": "Monitoring involves tracking specific metrics and events to understand the health and performance of a system. Observability, on the other hand, is about making it possible to gain insights into the internal state of a system by analyzing its outputs. While monitoring focuses on predefined metrics, observability enables deeper investigation into the underlying issues by leveraging logs, traces, and other data sources.",
        "reference_answer": "Monitoring and observability are related but distinct concepts. Monitoring focuses on collecting and analyzing metrics, such as the RED (Rate, Errors, Duration) and USE (Utilization, Saturation, Errors) metrics, to track the health and performance of a system. Observability, on the other hand, is a broader concept that encompasses monitoring, as well as other techniques like distributed tracing and log aggregation, to provide a more comprehensive understanding of a system's behavior and enable faster issue resolution.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 7873,
        "worker_latency_ms": 19382.89375009481,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "auto scaling in the cloud",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cost-optimization.md",
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/auto-scaling.md",
          "tests/fixtures/scale_docs/migration-strategies.md",
          "tests/fixtures/scale_docs/serverless-architecture.md"
        ],
        "worker_answer": "Auto-scaling is a technique for dynamically adjusting cloud resources to match workload demands. It involves automatically scaling your resources up or down based on predefined policies that monitor metrics like CPU utilization, memory usage, or queue length. This ensures you only pay for the resources needed at any given time and helps maintain optimal performance.",
        "reference_answer": "Auto-scaling in the cloud refers to the ability of a system to automatically adjust its resource capacity based on actual load. There are several strategies for implementing auto-scaling, including:\n\n1. Target Tracking Scaling: This adjusts the capacity to maintain a target value for a specific metric, such as average CPU utilization, keeping the target metric as close to the specified value as possible.\n\n2. Step Scaling: This adjusts the capacity based on a set of scaling adjustments, known as step adjustments, which specify a metric value range and the corresponding adjustment to make to the current capacity.\n\n3. Predictive Scaling: This uses load forecasting to automatically scale the capacity ahead of predicted traffic changes, allowing the system to proactively scale resources to meet anticipated demand.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 6231,
        "worker_latency_ms": 19592.785874963738,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "disaster recovery planning",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/disaster-recovery.md",
          "tests/fixtures/scale_docs/database-replication.md",
          "tests/fixtures/scale_docs/incident-response.md",
          "tests/fixtures/scale_docs/database-replication.md",
          "tests/fixtures/scale_docs/database-replication.md"
        ],
        "worker_answer": "Disaster recovery (DR) planning involves strategies to recover IT systems, data, and operations in the event of a disaster or major outage. Key strategies include backup and restore, pilot light, warm standby, and multi-site active-active architectures. Each strategy has tradeoffs in terms of cost, complexity, recovery time (RTO), and recovery point objectives (RPO). Effective DR planning is crucial for ensuring business continuity.",
        "reference_answer": "Disaster recovery planning involves strategies to recover IT systems, data, and operations in the event of a disaster or major outage. The two key metrics are Recovery Point Objective (RPO), the maximum amount of data that can be lost, and Recovery Time Objective (RTO), the maximum time before normal operations must resume. A fundamental strategy is backup and restore, which involves regularly creating full or incremental backups of critical data and systems that can be used to restore operations. Backups can be stored on-site, off-site, or in the cloud, and the specific backup strategy should be designed to meet the organization's RPO and RTO requirements.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 9689,
        "worker_latency_ms": 19409.799374989234,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "api gateway patterns",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/api-gateway-patterns.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/sample_docs/authentication.md",
          "tests/fixtures/scale_docs/load-balancing.md",
          "tests/fixtures/sample_docs/microservices.md"
        ],
        "worker_answer": "API Gateway Patterns include:\n\n1. Proxy Pattern - forwards requests directly to the appropriate microservice without additional processing.\n2. Aggregator Pattern - aggregates responses from multiple microservices and returns a combined result to the client.\n3. Translator Pattern - translates requests and responses between different data formats or protocols.\n4. Decomposer Pattern - decomposes client requests into individual service calls, executes them concurrently, and composes the final response.",
        "reference_answer": "The API gateway design patterns described in the document include:\n\n1. Proxy Pattern: The API gateway forwards requests directly to the appropriate microservice without any additional processing. This is suitable for simple use cases.\n\n2. Aggregator Pattern: The API gateway sends individual requests to relevant services, aggregates the responses, and returns the combined result to the client. This is used when a client needs to retrieve data from multiple microservices.\n\n3. Translator Pattern: The API gateway is responsible for translating the client's request into the format expected by the microservice, and then translating the response back into the client's expected format. This is used when the client and microservice APIs have different data formats or protocols.\n\n4. Decomposer Pattern: The API gateway decomposes the client's request into individual service calls, executes them concurrently, and then composes the final response. This is used when a client request requires data from multiple microservices, but the client does not want to be responsible for orchestrating those requests.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 6063,
        "worker_latency_ms": 20113.508833921514,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "cloud storage options",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cloud-storage-options.md",
          "tests/fixtures/scale_docs/cost-optimization.md",
          "tests/fixtures/scale_docs/ci-cd-pipelines.md",
          "tests/fixtures/scale_docs/cloud-storage-options.md",
          "tests/fixtures/scale_docs/secrets-management.md"
        ],
        "worker_answer": "The document discusses cloud storage options including object storage (Amazon S3), block storage (Amazon EBS), and file storage (Amazon EFS). It covers comparisons, lifecycle policies, and access patterns for these services.",
        "reference_answer": "Amazon S3 is an object storage service that offers several storage classes for different data access patterns and durability requirements. S3 Standard is for frequently accessed data, S3 Intelligent-Tiering automatically moves data between access tiers, S3 Standard-IA and S3 One Zone-IA are for infrequently accessed data, and S3 Glacier and S3 Glacier Deep Archive are for long-term archival of rarely accessed data. S3 Lifecycle policies can be used to automatically transition objects between storage classes or delete objects based on age. S3 supports various access methods like GET/HEAD/PUT/DELETE, multi-part uploads, batch operations, and data querying with S3 Select and S3 Glacier Select.",
        "score": {
          "correctness": 2,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.7143
        },
        "tokens_in_context": 4811,
        "worker_latency_ms": 19073.90541699715,
        "judge_raw_response": "{\"correctness\": 2, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "data serialization formats",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-serialization.md"
        ],
        "worker_answer": "The document discusses several popular data serialization formats, including JSON, Protobuf, Avro, Parquet, and ORC. It compares their performance, schema evolution capabilities, and usage scenarios to help in choosing the right format for specific needs.",
        "reference_answer": "Data serialization formats refer to the various ways of converting data structures or objects into a format that can be stored or transmitted, and then reconstructed later. Some popular data serialization formats include:\n\nJSON (JavaScript Object Notation): A lightweight, human-readable data interchange format that represents data as a collection of name/value pairs and ordered lists of values. It has a simple syntax, is language-independent, and is widely adopted, but can be verbose and has limited data types.\n\nProtobuf (Protocol Buffers): A binary serialization format developed by Google that is compact, fast, and language-neutral. It uses a schema definition language to define the structure of the data, enabling efficient schema evolution.\n\nAvro: A row-oriented data serialization system developed by Apache that provides rich data structures, a compact binary format, and dynamic typing. It supports schema evolution and is often used in big data and stream processing applications.\n\nParquet and ORC: Column-oriented file formats designed for efficient storage and querying of large datasets. They use compression and encoding techniques to achieve high performance and are commonly used in big data ecosystems like Hadoop and Spark.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 7077,
        "worker_latency_ms": 19984.933375031687,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "message queue patterns",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cloud-design-patterns.md",
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/websocket-protocol.md",
          "tests/fixtures/scale_docs/error-handling-patterns.md",
          "tests/fixtures/scale_docs/concurrency-patterns.md"
        ],
        "worker_answer": "The provided context discusses several message queue patterns including Queue-based Load Leveling and Competing Consumers. These patterns are used to manage the flow of messages in cloud-based applications, improving reliability and scalability.\n\n- **Queue-based Load Leveling Pattern**: This pattern smooths out high volumes of requests or tasks by placing them in a queue. It allows the system to handle bursts of activity without overloading downstream components.\n\n- **Competing Consumers Pattern**: This pattern allows multiple concurrent consumers to process messages from a queue or message broker, which can help improve the throughput and scalability of systems that need to process high volumes of messages.\n\nThese patterns are typically implemented using cloud services like Azure Storage Queues, Azure Service Bus Queues, RabbitMQ, or Apache Kafka.",
        "reference_answer": "Message queue patterns are a key component of event-driven architecture. The document describes the following relevant patterns:\n\n1. Event Producers: Components that generate events based on actions or state changes.\n2. Event Consumers: Components that subscribe to and process events.\n3. Event Bus: The middleware that facilitates the publishing and subscribing of events.\n\nThese patterns allow for asynchronous, decoupled communication between components in an event-driven system, promoting scalability, fault tolerance, and flexibility.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 4715,
        "worker_latency_ms": 22960.307249915786,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "functional programming concepts",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/functional-programming.md",
          "tests/fixtures/sample_docs/vector-databases.md",
          "tests/fixtures/scale_docs/design-patterns-creational.md",
          "tests/fixtures/scale_docs/design-patterns-creational.md",
          "tests/fixtures/scale_docs/design-patterns-creational.md"
        ],
        "worker_answer": "Functional Programming Concepts include:\n\n- **Immutability**: Values cannot be changed once created; instead, new values are created based on existing ones.\n- **Pure Functions**: Functions that return the same output for the same input and have no side effects.\n- **Monads**: Design patterns used to manage computational effects like error handling, optional values, and asynchronous operations.\n- **Functors**: Data structures that can be mapped over to apply functions to their values, creating new functors with transformed values.\n- **Pattern Matching**: Technique for decomposing and extracting data from complex structures, providing a concise way to handle different cases.\n- **Algebraic Data Types (ADTs)**: Custom data types that can represent a wide range of domain-specific concepts, including sum types (tagged unions) and product types (records).",
        "reference_answer": "Functional programming concepts include:\n\n1. Immutability: In functional programming, values are immutable, meaning they cannot be changed once created. Instead, new data structures are created with the desired changes.\n\n2. Pure functions: Pure functions are functions that, given the same input, will always return the same output and have no side effects. They do not depend on or modify any external state.\n\nThe source text provides details on the benefits of immutability, such as referential transparency, concurrency, and easier debugging and testing. It also explains the benefits of pure functions, including referential transparency, testability, parallelism, and caching/memoization.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 4657,
        "worker_latency_ms": 25110.886791022494,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "linux memory management",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/linux-memory-management.md",
          "tests/fixtures/scale_docs/systemd-services.md",
          "tests/fixtures/scale_docs/linux-performance-tuning.md"
        ],
        "worker_answer": "Linux memory management involves several key components including virtual memory, page tables, swap space, the OOM killer, huge pages, and NUMA. It manages memory allocation, deallocation, and the translation of virtual addresses to physical addresses using page tables. When a process attempts to access memory, if the required data is not present in physical memory, it triggers a page fault. The kernel then handles this by loading the necessary data from disk or swap space, updating the page tables accordingly. This system ensures efficient use of memory resources and maintains system stability even when physical memory is scarce.",
        "reference_answer": "Linux memory management involves the use of virtual memory, which allows processes to access more memory than is physically available on the system. This is implemented through page tables, which map virtual memory pages to physical memory frames. The processor's Memory Management Unit (MMU) uses these page tables to translate virtual addresses to physical addresses during memory accesses. Page tables are organized in a multi-level hierarchy, with a top-level page directory and lower-level page tables. When a process attempts to access a virtual address, the MMU uses the page tables to translate the virtual address to a physical address. If the page is not present in the page tables, the MMU generates a page fault, which is handled by the kernel.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 6557,
        "worker_latency_ms": 23699.77966696024,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "dns resolution process",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/dns-resolution.md",
          "tests/fixtures/scale_docs/network-troubleshooting.md",
          "tests/fixtures/scale_docs/package-management.md",
          "tests/fixtures/scale_docs/cdn-architecture.md",
          "tests/fixtures/scale_docs/penetration-testing-methodology.md"
        ],
        "worker_answer": "The DNS resolution process involves several steps:\n\n1. **Client Lookup**: The client checks its local cache for the domain name's IP address.\n2. **Recursive Query**: If not found, the client sends a recursive query to its configured DNS resolver.\n3. **Iterative Queries**: The resolver queries the root DNS servers, TLD servers, and finally the authoritative DNS server for the domain.\n4. **Authoritative Server Lookup**: The resolver queries the authoritative DNS server to retrieve the IP address or other requested record.\n5. **Response to Client**: The resolver caches the result and sends it back to the client.\n\nThis process allows translation of domain names into IP addresses, enabling internet services like web browsing and email delivery.",
        "reference_answer": "The DNS resolution process involves the following steps:\n\n1. The client (e.g., web browser) first checks its local cache for the requested domain's IP address. If not found, it sends a recursive query to a DNS resolver.\n\n2. The resolver queries the root DNS servers to find the appropriate top-level domain (TLD) server, then queries the TLD server to locate the authoritative DNS server for the domain.\n\n3. The authoritative server provides the requested information, such as the IP address, which the resolver then caches and returns to the client.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 4862,
        "worker_latency_ms": 23599.484541919082,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "connection pooling benefits",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/connection-pooling.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/http2-http3.md",
          "tests/fixtures/scale_docs/database-replication.md"
        ],
        "worker_answer": "Database connection pooling provides several benefits, including:\n\n- Improved performance by reducing the overhead of creating new database connections for every operation.\n- Enhanced scalability as it allows applications to handle more concurrent requests efficiently.\n- Better resource utilization by reusing existing database connections instead of opening and closing new ones frequently.",
        "reference_answer": "Database connection pooling provides several benefits:\n\n1. Improved performance: By reusing existing database connections instead of creating new ones for every operation, connection pooling reduces the overhead and latency associated with establishing new connections.\n\n2. Increased scalability: Connection pooling allows an application to handle more concurrent requests by efficiently managing a limited number of database connections.\n\n3. Efficient resource utilization: Connection pooling optimizes the use of database server resources by maintaining a pool of reusable connections, reducing the need to create and destroy connections frequently.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 4541,
        "worker_latency_ms": 19644.992457935587,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "cryptography basics",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cryptography-basics.md",
          "tests/fixtures/scale_docs/ssh-configuration.md"
        ],
        "worker_answer": "Cryptography fundamentals include:\n\n- **Symmetric Encryption**: Uses a single shared secret key for both encryption and decryption. Examples include AES (Advanced Encryption Standard) and ChaCha20.\n- **Asymmetric Encryption**: Uses a pair of keys, one public for encryption and one private for decryption. Examples include RSA (Rivest-Shamir-Adleman).\n- **Hashing**: Converts data of arbitrary size into a fixed-size string of bytes, typically for verifying the integrity or authenticity of data. Examples include SHA-256.\n- **Key Derivation Functions (KDFs)**: Generates cryptographic keys from a secret information like a password.",
        "reference_answer": "Cryptography is the practice of securing information through the use of codes and ciphers. The fundamental concepts of cryptography include:\n\n- Symmetric encryption: A single shared secret key is used to encrypt and decrypt data, such as the Advanced Encryption Standard (AES) algorithm. AES supports 128, 192, and 256-bit key sizes and operates on 128-bit blocks of data.\n- Asymmetric encryption: Two separate keys are used - a public key for encryption and a private key for decryption, such as RSA and ECDSA.\n- Hashing: A one-way mathematical function that converts data into a fixed-size output, such as SHA-256 and bcrypt.\n- Key derivation functions (KDFs): Algorithms that derive secure cryptographic keys from a password or other input, such as PBKDF2 and Argon2.\n\nThese fundamental cryptographic concepts are widely used to secure data, communications, and user authentication.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 6595,
        "worker_latency_ms": 25278.03416608367,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "distributed transactions",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/monitoring-observability.md",
          "tests/fixtures/sample_docs/caching.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md"
        ],
        "worker_answer": "Distributed transaction across services involves steps like creating an order, processing payment, and reserving items. If any step fails, compensating transactions are used to rollback the operations that were successful, ensuring consistency across services. This is often implemented using patterns like the Saga Pattern.",
        "reference_answer": "Distributed transactions refer to the coordination of multiple operations across different systems or databases as a single logical transaction. The source text does not directly discuss distributed transactions, as it focuses on concurrency patterns and constructs within a single system, such as thread pools. The text provides an example implementation of a basic thread pool in Python, but does not mention how to handle distributed transactions or the challenges involved in coordinating multiple systems. The source text does not contain information to answer the query about distributed transactions.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 1433,
        "worker_latency_ms": 9847.61937498115,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "container security hardening",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/network-security-hardening.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/ssh-configuration.md"
        ],
        "worker_answer": "Container security hardening includes several key practices such as image scanning, runtime protection, seccomp, AppArmor, rootless containers, and supply chain security. These measures help in identifying vulnerabilities, securing the runtime environment, and ensuring that container images are free from known issues.",
        "reference_answer": "Container security hardening involves several key practices:\n\n1. Image scanning: Scanning container images for known vulnerabilities using tools like Trivy, Clair, and Anchore, and scanning for malware using tools like ClamAV.\n2. Runtime protection: Implementing security measures like seccomp and AppArmor to restrict the capabilities of running containers and prevent unauthorized access or actions.\n3. Rootless containers: Using rootless containers, which run as non-root users, to reduce the risk of privilege escalation attacks.\n4. Supply chain security: Ensuring the security of the container image supply chain by verifying the provenance and integrity of container images.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 5081,
        "worker_latency_ms": 19710.859457962215,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "well architected framework",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/stream-processing.md"
        ],
        "worker_answer": "The AWS Well-Architected Framework is detailed technical documentation that covers six pillars aimed at helping users design and operate reliable, secure, efficient, and cost-effective systems on AWS. The pillars include Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability (though the provided context does not cover all pillars explicitly). Each pillar includes design principles, key areas to focus on, and examples of how to implement best practices using AWS services.",
        "reference_answer": "The AWS Well-Architected Framework is a set of best practices and design principles to help cloud architects build secure, high-performing, resilient, and efficient infrastructure. The Operational Excellence pillar focuses on the ability to run and monitor systems to deliver business value and continually improve supporting processes and procedures. Key design principles include automating operational procedures, embedding documentation, making frequent and reversible changes, and anticipating and learning from failures. The framework covers areas such as implementing strong change management, maintaining observability, having effective incident response, and continuously improving operations based on data.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 7659,
        "worker_latency_ms": 20681.740542058833,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "cost optimization strategies",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cost-optimization.md",
          "tests/fixtures/scale_docs/sql-query-optimization.md",
          "tests/fixtures/scale_docs/migration-strategies.md",
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/serverless-architecture.md"
        ],
        "worker_answer": "The document outlines several cloud cost optimization strategies including right-sizing, reserved instances, spot instances, auto-scaling, and storage tiering. Right-sizing involves ensuring cloud resources are appropriately sized to match workload requirements without being oversized or undersized. Reserved Instances offer upfront commitments for lower hourly rates on resources, providing cost savings compared to on-demand pricing. Spot Instances are discounted excess compute capacity that can be interrupted, offering cost savings for flexible workloads. Auto-scaling dynamically adjusts resources based on workload demands to ensure efficient resource utilization. Storage tiering involves organizing data across different storage media based on access frequency and performance requirements to optimize costs.",
        "reference_answer": "Cloud cost optimization strategies include:\n\n1. Right-sizing: Ensuring cloud resources are appropriately sized to meet workload requirements. This involves identifying and resizing oversized resources to better match usage needs.\n\n2. Reserved Instances: Committing to a longer-term usage of cloud resources in exchange for a discounted hourly rate. This can provide significant cost savings compared to on-demand pricing.\n\n3. Spot Instances: Utilizing spare cloud computing capacity at discounted prices. Spot instances can offer substantial cost savings but are subject to interruption.\n\n4. Auto-scaling: Automatically scaling cloud resources up and down based on demand to optimize costs and ensure efficient resource utilization.\n\n5. Storage tiering: Leveraging different storage tiers (e.g., hot, warm, cold) to match data access requirements and minimize storage costs.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 5063,
        "worker_latency_ms": 23041.35870898608,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "incident response steps",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/incident-response.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/security-audit-checklist.md",
          "tests/fixtures/scale_docs/compliance-frameworks.md"
        ],
        "worker_answer": "The incident response steps outlined in the provided context are:\n\n1. Detection: The initial identification of an incident.\n2. Triaging: Assessing the severity, impact, and urgency to determine the appropriate response.\n3. Escalation: Notifying relevant teams or on-call personnel.\n4. Remediation: Investigating, diagnosing, and resolving the incident.\n5. Postmortem: Reviewing the incident to identify root causes and document lessons learned.\n6. Closure: Formally closing the incident after necessary actions have been completed.",
        "reference_answer": "The key incident response steps outlined in the document are:\n\n1. Detection: Identifying the incident, typically through monitoring systems or user reports.\n2. Triaging: Assessing the incident's severity, impact, and urgency to determine the appropriate response.\n3. Escalation: Notifying the relevant teams or on-call personnel to initiate the incident response process.\n4. Remediation: Investigating, diagnosing, and resolving the incident.\n5. Postmortem: Reviewing the incident, identifying root causes, and documenting lessons learned.\n6. Closure: Formally closing the incident after all necessary actions have been completed.\n\nThe document also describes four incident severity levels, ranging from critical (Severity 1) to low (Severity 4), which determine the response and prioritization of the incident.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 6310,
        "worker_latency_ms": 21071.99479197152,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "multi-region deployment",
        "category": "semantic",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/multi-region-deployment.md",
          "tests/fixtures/scale_docs/ci-cd-pipelines.md",
          "tests/fixtures/scale_docs/ci-cd-pipelines.md"
        ],
        "worker_answer": "A multi-region deployment strategy involves deploying applications and services across multiple geographic regions to improve availability, reduce latency, and provide disaster recovery capabilities. This guide covers active-active vs active-passive architectures, data replication techniques (synchronous, asynchronous, eventual consistency), and DNS failover mechanisms. Each approach has tradeoffs related to complexity, cost, performance, and consistency requirements.",
        "reference_answer": "Multi-region deployment refers to the strategy of deploying an application or service across multiple geographic regions. This can be done in an active-active or active-passive configuration.\n\nIn an active-active setup, the application runs in multiple regions simultaneously, with all regions actively serving user traffic. This provides high availability and low latency, as users can be routed to the nearest region. However, it requires more complex coordination and data replication between regions.\n\nAlternatively, an active-passive configuration has one region actively serving traffic, while the other region(s) are in standby mode, ready to take over in the event of a failover. This is simpler to implement but offers lower availability and potentially higher latency.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 7861,
        "worker_latency_ms": 19303.727416903712,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "ssh configuration security",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/ssh-configuration.md",
          "tests/fixtures/scale_docs/firewall-rules.md",
          "tests/fixtures/scale_docs/api-security.md",
          "tests/fixtures/scale_docs/infrastructure-as-code.md",
          "tests/fixtures/scale_docs/network-security-hardening.md"
        ],
        "worker_answer": "To improve the security of your SSH setup, you can apply the following measures:\n\n1. Use Strong Encryption: Specify strong ciphers, MACs, and key exchange algorithms in your `~/.ssh/config` file.\n2. Disable Password Authentication: Set `PasswordAuthentication no` and rely on public-key authentication by setting `PubkeyAuthentication yes`.\n3. Disable Root Login: Prevent direct root login to your SSH server by setting `PermitRootLogin no`.\n4. Limit Allowed Users: Specify a list of allowed users with the `AllowUsers` directive.\n5. Enable Logging: Increase logging verbosity to help with troubleshooting and security monitoring by setting `LogLevel INFO`.\n6. Use Trusted DNS: Specify a trusted DNS server in your SSH configuration to prevent man-in-the-middle attacks.",
        "reference_answer": "The SSH configuration file allows you to define custom settings for connecting to remote hosts, including specifying the host, user, identity file, and port. It also supports features like ProxyCommand to connect through a bastion host. SSH authentication relies on public-key cryptography, where users have a public key shared with remote servers and a private key kept secure on their local machine. To manage multiple keys, you can use the IdentityFile option in the SSH config file to specify which key to use for each host. The SSH agent is a background process that holds your private keys in memory, allowing you to authenticate with remote servers without constantly entering your passphrase.",
        "score": {
          "correctness": 1,
          "completeness": 2,
          "hallucination": 0,
          "combined": 0.4286
        },
        "tokens_in_context": 6828,
        "worker_latency_ms": 26090.71916702669,
        "judge_raw_response": "{\"correctness\": 1, \"completeness\": 2, \"hallucination\": 0}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "vpn tunneling protocols",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/scale_docs/ssh-configuration.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/scale_docs/http2-http3.md"
        ],
        "worker_answer": "The context discusses several VPN tunneling protocols including IPSec, WireGuard, and OpenVPN. These protocols operate at different layers of the OSI model:\n\n- **IPSec**: Operates at Layer 3 (network layer) and uses tunneling modes like Transport Mode for host-to-host connections and Tunnel Mode for network-to-network (site-to-site) connections.\n- **WireGuard**: Also operates at Layer 3 and uses public-key cryptography for secure tunneling.\n- **OpenVPN**: Operates at Layer 7 (application layer) and supports both routed (\"tunneling\") and bridged configurations.\n\nThese protocols encapsulate the payload protocol within another protocol to transmit it securely over a public network.",
        "reference_answer": "VPN tunneling protocols refer to the various methods used to establish secure, encrypted connections between devices or networks over a public network like the internet. The two main VPN protocols discussed in the source text are:\n\nIPSec (Internet Protocol Security): IPSec operates at the network layer and provides encryption, authentication, and data integrity for IP traffic. It has two main modes: Transport Mode, which encrypts the payload but leaves the original IP headers intact, and Tunnel Mode, which encapsulates the entire original IP packet within a new IP packet with IPSec headers. IPSec uses the ESP and AH protocols, as well as the IKE protocol for key exchange.\n\nWireGuard: WireGuard is a newer, open-source VPN protocol that aims to be faster, simpler, and more secure than existing solutions. It operates at the network layer and uses modern cryptographic primitives like Curve25519 and ChaCha20-Poly1305. WireGuard has a minimal attack surface and uses a peer-to-peer architecture, allowing devices to communicate directly without a central VPN server.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 2938,
        "worker_latency_ms": 25629.275416955352,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "websocket protocol overview",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/websocket-protocol.md",
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md",
          "tests/fixtures/scale_docs/http2-http3.md"
        ],
        "worker_answer": "The WebSocket protocol is a computer communications protocol that provides a two-way communication channel over a single TCP connection. It was designed as an efficient alternative to traditional HTTP-based polling for real-time web applications, allowing for bidirectional communication between client and server.",
        "reference_answer": "The WebSocket protocol is a computer communications protocol that provides a two-way communication channel over a single TCP connection. It was designed as an efficient alternative to traditional HTTP-based polling for real-time web applications.\n\nThe WebSocket handshake is the process of establishing a WebSocket connection between a client and a server. The client initiates the handshake with an HTTP request that includes specific headers, and the server responds with a set of headers indicating that it is switching the protocol to WebSocket.\n\nAfter the handshake is complete, the client and server can start exchanging WebSocket frames, which are the basic units of data transmission in the WebSocket protocol. Each frame has a specific structure, including fields for the frame type, payload length, and payload data.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 10226,
        "worker_latency_ms": 19625.28779206332,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "helm charts fundamentals",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/helm-charts.md",
          "tests/fixtures/scale_docs/container-fundamentals.md",
          "tests/fixtures/scale_docs/helm-charts.md"
        ],
        "worker_answer": "Helm charts are the unit of packaging for Helm, a package manager for Kubernetes. They simplify the deployment and management of applications by allowing you to define, install, and upgrade complex Kubernetes applications. A Helm chart consists of templates written in the Go template language that generate Kubernetes manifest files, values for those templates defined in `values.yaml`, and can include dependencies on other Helm charts. Additionally, hooks allow for specific actions during the release lifecycle, and testing can be performed using Helm's built-in commands. Charts are typically stored in repositories for distribution and management.",
        "reference_answer": "Helm charts are the unit of packaging for the Helm package manager for Kubernetes. The key fundamentals of Helm charts include:\n\nTemplates: Helm templates are written in the Go template language and used to generate Kubernetes manifest files. They can include variables, functions, and control structures for dynamic and reusable configurations.\n\nValues: The `values.yaml` file defines the default values for the chart's templates, which can be overridden when the chart is installed or upgraded.\n\nDependencies: Helm charts can depend on other Helm charts, called \"subchart\" dependencies, which are specified in the `Chart.yaml` file.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 2085,
        "worker_latency_ms": 18256.19991600979,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "linux process management",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/linux-process-management.md",
          "tests/fixtures/scale_docs/linux-performance-tuning.md",
          "tests/fixtures/scale_docs/package-management.md",
          "tests/fixtures/scale_docs/linux-memory-management.md",
          "tests/fixtures/scale_docs/systemd-services.md"
        ],
        "worker_answer": "Linux Process Management covers the following topics:\n\n- Fork/exec: Creating new processes and executing different programs.\n- Process states: Running, Waiting, Stopped, Zombie.\n- Signals: Mechanism for sending notifications to processes; common signals include SIGINT, SIGTERM, SIGKILL, and SIGCHLD.\n- Nice/renice: Adjusting the scheduling priority of processes to manage system resources.\n- Cgroups: Allocating and managing CPU, memory, and I/O resources among hierarchical groups of processes.\n- Namespaces: Isolating system resources to create virtual environments for different groups of processes.",
        "reference_answer": "Linux process management involves the creation, execution, and monitoring of processes. The key aspects are:\n\n1. Fork and Exec: The `fork()` system call creates a new process (child) that is a copy of the calling process (parent). The `exec()` system call replaces the current process image with a new process image, effectively terminating the old process and starting a new one.\n\n2. Process States: Processes in Linux can be in one of several states - running, waiting, stopped, or zombie. The `ps` command can be used to view the current state of processes.\n\n3. Signals: Processes can receive and handle various signals, which are software interrupts that notify a process of an event. Signals can be used to control and manage processes.\n\n4. Nice and Renice: The `nice` and `renice` commands allow you to adjust the scheduling priority of a process, which determines how much CPU time it receives.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 7170,
        "worker_latency_ms": 25286.11245902721,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "network troubleshooting tools",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/network-troubleshooting.md",
          "tests/fixtures/scale_docs/network-security-hardening.md"
        ],
        "worker_answer": "The document mentions several network troubleshooting tools including tcpdump, traceroute, netstat, ss, dig, and nslookup. Each tool serves different purposes in diagnosing network issues:\n\n- `tcpdump`: Captures and analyzes network traffic.\n- `traceroute`: Identifies the path packets take to reach a destination.\n- `netstat`: Provides information about network connections and interfaces.\n- `ss`: Offers similar functionality to netstat but with improved performance and more detailed information about network sockets.\n- `dig`: Queries DNS servers for domain name resolution details.\n- `nslookup`: Another tool used to query DNS servers and retrieve information about domain names.\n\nThese tools are described in detail within the document for troubleshooting network issues.",
        "reference_answer": "The key network troubleshooting tools mentioned in the source text are:\n\n1. tcpdump: A powerful network packet analysis tool that allows capturing and inspecting network traffic on a specific interface. It can be used for troubleshooting network issues, analyzing network behavior, and identifying potential security threats.\n\n2. traceroute: A network diagnostic tool that helps identify the path a packet takes from your computer to a destination IP address or hostname. It is useful for troubleshooting connectivity issues and identifying where in the network a problem might be occurring.\n\nThe text provides details on the basic usage and common use cases for each of these tools, including examples of how to use them for tasks like capturing and analyzing network traffic, troubleshooting connectivity issues, identifying security threats, and debugging application-level issues.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 7991,
        "worker_latency_ms": 25673.421833082102,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "database replication types",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/database-replication.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md"
        ],
        "worker_answer": "Database replication involves maintaining multiple copies of a database on different servers or nodes. The main types discussed in the context are:\n\n1. **Primary-Replica (Master-Slave) Replication**: In this model, there is a single primary database that accepts all write queries. Changes are asynchronously replicated to one or more read-only replica databases.\n\n2. **Multi-Primary (Multi-Master) Replication**: This architecture allows multiple primary databases to accept write queries. Changes are replicated bidirectionally between all nodes, and a conflict resolution process is required to handle any conflicts.\n\nThese types of replication offer different trade-offs in terms of availability, consistency, and complexity.",
        "reference_answer": "Database replication can take several forms:\n\nPrimary-replica (or master-slave) replication has a single primary database that accepts all writes, and changes are asynchronously replicated to one or more read-only replica databases. This provides high availability, scalability for reads, and disaster recovery.\n\nMulti-primary (or multi-master) replication allows writes on multiple databases, with conflicts resolved automatically or manually. This supports higher write throughput but introduces more complexity around conflict resolution.\n\nSynchronous replication ensures that changes are applied atomically across all replicas, providing stronger consistency guarantees but potentially lower availability during failures.\n\nAsynchronous replication, the more common approach, allows the replicas to lag behind the primary, improving availability but potentially leading to data loss if the primary fails.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 7884,
        "worker_latency_ms": 21584.16270907037,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "nosql design patterns",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/nosql-patterns.md",
          "tests/fixtures/sample_docs/microservices.md"
        ],
        "worker_answer": "NoSQL data modeling includes several key patterns and techniques such as denormalization, aggregation, time-series data modeling, graph traversal patterns, and document design principles. These patterns are designed to optimize for read performance, query efficiency, and data access patterns specific to NoSQL databases. Denormalization minimizes the need for joins by duplicating data, while aggregation groups related data into self-contained units to enable atomic updates and simplify queries. Time-series data modeling is optimized for handling high volumes of time-based measurements, allowing efficient storage and querying. Graph traversal patterns are used in graph databases to navigate relationships between entities efficiently. Document design principles focus on structuring data within a document database to match access patterns and improve performance.",
        "reference_answer": "NoSQL databases employ flexible, schema-less or schema-optional models that can accommodate unstructured, semi-structured, and rapidly changing data. Key NoSQL data modeling patterns include:\n\n1. Denormalization: NoSQL encourages denormalization to optimize for read performance and query patterns, minimizing the need for complex joins.\n2. Aggregation: NoSQL data models are structured to match common query patterns, even if it results in duplicating data across different entities.\n3. Time-series data modeling: NoSQL databases are well-suited for time-series data, allowing efficient storage and retrieval of data points over time.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 5624,
        "worker_latency_ms": 24881.345374975353,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "sql query optimization",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/sql-query-optimization.md",
          "tests/fixtures/scale_docs/owasp-top-10.md",
          "tests/fixtures/scale_docs/nosql-patterns.md",
          "tests/fixtures/scale_docs/postgresql-internals.md",
          "tests/fixtures/scale_docs/database-sharding.md"
        ],
        "worker_answer": "SQL query optimization involves several techniques such as using EXPLAIN plans to analyze query execution, implementing effective index strategies, rewriting queries for better performance, choosing the right join algorithms, and analyzing database statistics. These methods aim to reduce query execution time by minimizing resource usage and improving data retrieval efficiency.",
        "reference_answer": "The key techniques for SQL query optimization include:\n\n1. Using EXPLAIN plans to analyze the execution plan and identify potential performance bottlenecks, such as full table scans or suboptimal join algorithms.\n2. Selecting appropriate indexes to speed up data lookups, especially for columns used in the WHERE clause.\n3. Rewriting queries to use more efficient join algorithms, such as hash joins or merge joins, instead of nested loop joins.\n4. Analyzing database statistics to ensure the query optimizer has accurate information about the data distribution and cardinality, which can help it generate better execution plans.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 6483,
        "worker_latency_ms": 18710.27329203207,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "data pipeline design",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-pipeline-design.md",
          "tests/fixtures/scale_docs/ci-cd-pipelines.md",
          "tests/fixtures/scale_docs/ci-cd-pipelines.md",
          "tests/fixtures/scale_docs/ci-cd-pipelines.md",
          "tests/fixtures/scale_docs/ci-cd-pipelines.md"
        ],
        "worker_answer": "A data pipeline is designed to transform, integrate, and move data from various sources to a target destination like a data warehouse or analytics platform. Key components include Data Sources, Ingestion, Transformation, Loading, Orchestration, and Monitoring & Alerting. Design considerations involve handling data volume and velocity, accommodating data variety, ensuring data quality, scalability, reliability, performance, maintainability, and security. Architectural patterns include Batch Processing, Streaming Processing, Hybrid Processing, and Lambda Architecture. The choice between batch and streaming processing depends on the specific requirements of latency, fault tolerance, and real-time needs.",
        "reference_answer": "A data pipeline is a series of steps that transform, integrate, and move data from various sources to a target destination. The key components of a data pipeline include data sources, ingestion, transformation, loading, orchestration, and monitoring and alerting. When designing a data pipeline, important considerations include data volume and velocity, data variety, data quality, scalability, reliability, performance, maintainability, and security and governance. The pipeline should be designed to handle the expected data load, accommodate diverse data formats, ensure data integrity, scale as needed, and be resilient, efficient, and easy to maintain.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 10177,
        "worker_latency_ms": 22238.827583030798,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "testing strategies for software",
        "category": "semantic",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/testing-strategies.md",
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/penetration-testing-methodology.md",
          "tests/fixtures/scale_docs/penetration-testing-methodology.md"
        ],
        "worker_answer": "The document outlines several testing strategies crucial for ensuring the quality and reliability of software systems:\n\n1. **Unit Testing**: Focuses on verifying individual components or units of the system in isolation.\n2. **Integration Testing**: Evaluates how different parts of the system work together, including interactions and data flow between components.\n3. **End-to-End (E2E) Testing**: Validates the entire system, including user interactions and data flow through the application from a user's perspective.\n\nEach strategy serves to ensure different aspects of software quality, with unit tests checking individual parts, integration tests ensuring components work together correctly, and E2E tests confirming the system functions as expected from start to finish.",
        "reference_answer": "The primary testing strategies for software include:\n\n1. Unit Testing: Verifying the correctness of individual, isolated components or units of the system.\n2. Integration Testing: Evaluating how different components or modules of the system work together.\n3. End-to-End (E2E) Testing: Validating the entire system, including user interactions and the flow of data through the application.\n\nUnit tests focus on exercising a single, isolated component or functionality, ensuring it behaves as expected. Integration tests examine the interactions and data flow between different components or modules, simulating complete end-to-end workflows. These testing strategies play a crucial role in ensuring the overall quality and stability of the software system.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 8413,
        "worker_latency_ms": 23249.078207998537,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "dependency injection benefits",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/dependency-injection.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/scale_docs/owasp-top-10.md",
          "tests/fixtures/scale_docs/systemd-services.md",
          "tests/fixtures/scale_docs/database-replication.md"
        ],
        "worker_answer": "The key benefits of using dependency injection include:\n\n1. **Testability**: Easier to write unit tests as you can substitute mock or stub implementations of dependencies.\n2. **Flexibility**: Enables swapping out implementations, enhancing maintainability and flexibility.\n3. **Modularity**: Promotes a modular design where components can be independently developed, tested, and deployed.\n4. **Inversion of Control**: Allows a central authority (IoC container) to manage object creation and lifetime, inverting control from the objects themselves.",
        "reference_answer": "Dependency injection provides several key benefits:\n\n1. Testability: It makes it easier to write unit tests by allowing you to easily substitute mock or stub implementations of dependencies.\n2. Flexibility: Decoupling objects from their dependencies enables you to easily swap out implementations, improving flexibility and maintainability.\n3. Modularity: Dependency injection promotes a modular design, as components can be developed, tested, and deployed independently.\n4. Inversion of Control: Dependency injection inverts the control of object creation and lifetime management, allowing a central authority (the IoC container) to manage these concerns.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 5920,
        "worker_latency_ms": 20447.252165991813,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "behavioral design patterns",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/design-patterns-behavioral.md",
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/design-patterns-structural.md",
          "tests/fixtures/scale_docs/design-patterns-creational.md"
        ],
        "worker_answer": "The provided context discusses several Behavioral Design Patterns including the Observer, Strategy, Command, State, Template Method, Iterator, and Mediator patterns. It provides details on their structure, usage scenarios, advantages, disadvantages, and example implementations in Java for some of these patterns.",
        "reference_answer": "Behavioral design patterns are a category of design patterns that focus on the communication between objects and how they interact with each other. Some key behavioral design patterns include:\n\n- Observer: Allows objects to be notified of changes to the state of another object. It provides a way to implement event-driven architectures.\n- Strategy: Defines a family of algorithms, encapsulates each one, and makes them interchangeable. Clients can select the appropriate algorithm at runtime.\n- Command: Encapsulates a request as an object, allowing for the parameterization of clients with different requests, queue or log requests, and support undoable operations.\n\nThese patterns provide flexible ways to manage object interactions and communication, making the code more modular, extensible, and maintainable.",
        "score": {
          "correctness": 3,
          "completeness": 2,
          "hallucination": 1,
          "combined": 0.8571
        },
        "tokens_in_context": 7616,
        "worker_latency_ms": 19021.224834024906,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 2, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "creational design patterns",
        "category": "semantic",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/design-patterns-creational.md",
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/design-patterns-structural.md",
          "tests/fixtures/scale_docs/design-patterns-behavioral.md",
          "tests/fixtures/scale_docs/design-patterns-behavioral.md"
        ],
        "worker_answer": "The provided context discusses several creational design patterns including Singleton, Factory Method, Abstract Factory, Builder, and Prototype. These patterns are used to manage the creation of objects in a way that is flexible and maintainable, providing solutions for problems such as ensuring only one instance of a class exists (Singleton), decoupling object creation from usage (Factory Method, Abstract Factory), and managing complex object construction processes (Builder). Each pattern has its specific use cases and benefits, such as promoting code reusability, modularity, and ease of extension.",
        "reference_answer": "The creational design patterns are a set of design patterns that deal with object creation mechanisms, aiming to create objects in a manner suitable to the situation. The key creational design patterns are:\n\n- Singleton: Ensures a class has only one instance and provides a global point of access to it.\n- Factory Method: Defines an interface for creating an object, but lets subclasses decide which class to instantiate.\n- Abstract Factory: Provides an interface for creating families of related or dependent objects without specifying their concrete classes.\n- Builder: Separates the construction of a complex object from its representation so that the same construction process can create different representations.\n- Prototype: Specifies the kind of object to create using a prototypical instance, and creates new objects by copying this prototype.\n\nThese patterns provide different ways to create objects, allowing for flexibility, modularity, and testability in software design.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 8779,
        "worker_latency_ms": 21020.178625010885,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "how to bake sourdough in a kubernetes cluster",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/kubernetes-architecture.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about baking sourdough in a Kubernetes cluster. It focuses on the architecture, components, and resources of Kubernetes.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 6010,
        "worker_latency_ms": 17220.36366595421,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "best brand of hiking boots for Mars",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/package-management.md",
          "tests/fixtures/scale_docs/api-security.md",
          "tests/fixtures/scale_docs/linux-performance-tuning.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 3060,
        "worker_latency_ms": 22062.43920896668,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "quantum entanglement in photosynthesis",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/functional-programming.md",
          "tests/fixtures/scale_docs/http2-http3.md",
          "tests/fixtures/scale_docs/http2-http3.md",
          "tests/fixtures/scale_docs/functional-programming.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 3226,
        "worker_latency_ms": 17730.732000083663,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "the secret to eternal life according to python docs",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/cryptography-basics.md",
          "tests/fixtures/scale_docs/testing-strategies.md",
          "tests/fixtures/scale_docs/secrets-management.md",
          "tests/fixtures/scale_docs/concurrency-patterns.md",
          "tests/fixtures/scale_docs/error-handling-patterns.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 3527,
        "worker_latency_ms": 18476.162417093292,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "where did I leave my keys",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/api-security.md",
          "tests/fixtures/scale_docs/tls-ssl.md",
          "tests/fixtures/sample_docs/caching.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/sample_docs/database-design.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 1112,
        "worker_latency_ms": 6815.3487499803305,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "recipe for disaster in distributed systems",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/database-replication.md",
          "tests/fixtures/scale_docs/cloud-storage-options.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/monitoring-observability.md",
          "tests/fixtures/scale_docs/database-replication.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain a recipe for disaster in distributed systems. It discusses considerations for failover and disaster recovery, benefits and challenges of eventual consistency, and monitoring strategies but does not provide a recipe for creating a disaster.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 2166,
        "worker_latency_ms": 14249.657749896869,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "blue whales vs sperm whales",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/database-migrations.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/ci-cd-pipelines.md",
          "tests/fixtures/scale_docs/grpc-protocol.md",
          "tests/fixtures/scale_docs/well-architected-framework.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain any information about blue whales vs sperm whales.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 2269,
        "worker_latency_ms": 14849.800665979274,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "history of the Roman Empire",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-warehouse-modeling.md",
          "tests/fixtures/scale_docs/penetration-testing-methodology.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/data-quality.md",
          "tests/fixtures/scale_docs/data-lake-architecture.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 2583,
        "worker_latency_ms": 14449.71195794642,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "how to play the banjo",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-lake-architecture.md",
          "tests/fixtures/scale_docs/sql-query-optimization.md",
          "tests/fixtures/scale_docs/helm-charts.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/spark-fundamentals.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information on how to play the banjo.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 2388,
        "worker_latency_ms": 17449.079417041503,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "why is the sky blue",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/database-migrations.md",
          "tests/fixtures/scale_docs/ci-cd-pipelines.md",
          "tests/fixtures/scale_docs/grpc-protocol.md",
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/ci-cd-pipelines.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 1457,
        "worker_latency_ms": 7582.738417084329,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "symptoms of a cold",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/incident-response.md",
          "tests/fixtures/scale_docs/cost-optimization.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/testing-strategies.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 1948,
        "worker_latency_ms": 11144.13016603794,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "movie recommendations for tonight",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/scale_docs/incident-response.md",
          "tests/fixtures/scale_docs/container-security.md",
          "tests/fixtures/scale_docs/penetration-testing-methodology.md",
          "tests/fixtures/scale_docs/container-security.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 2309,
        "worker_latency_ms": 12988.924499950372,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "stock market predictions for 2026",
        "category": "adversarial",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-lake-architecture.md",
          "tests/fixtures/scale_docs/elasticsearch-basics.md",
          "tests/fixtures/scale_docs/chaos-engineering.md",
          "tests/fixtures/scale_docs/concurrency-patterns.md",
          "tests/fixtures/scale_docs/auto-scaling.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 3625,
        "worker_latency_ms": 18209.74608405959,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "philosophy of mind and AI",
        "category": "adversarial",
        "difficulty": "hard",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/sample_docs/semantic-search.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 1818,
        "worker_latency_ms": 9995.015249936841,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "meaning of life 42",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/linux-process-management.md",
          "tests/fixtures/sample_docs/semantic-search.md",
          "tests/fixtures/scale_docs/helm-charts.md",
          "tests/fixtures/scale_docs/firewall-rules.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 1764,
        "worker_latency_ms": 10064.6441669669,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "how to win friends and influence people",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/nosql-patterns.md",
          "tests/fixtures/scale_docs/well-architected-framework.md",
          "tests/fixtures/scale_docs/elasticsearch-basics.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/sample_docs/semantic-search.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information on how to win friends and influence people. It discusses NoSQL data modeling patterns, graph database traversal in Neo4j, AWS Well-Architected Framework pillars, Elasticsearch features, zero trust architecture principles in hybrid and multi-cloud environments, and semantic search techniques. None of these topics relate to the question asked about winning friends and influencing people.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 2266,
        "worker_latency_ms": 16334.361374960281,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "yoga for beginners",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/semantic-search.md",
          "tests/fixtures/scale_docs/design-patterns-creational.md",
          "tests/fixtures/scale_docs/design-patterns-creational.md",
          "tests/fixtures/scale_docs/design-patterns-creational.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 1408,
        "worker_latency_ms": 7511.643999954686,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "best coffee shops in London",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/shell-scripting.md",
          "tests/fixtures/scale_docs/vpn-tunneling.md",
          "tests/fixtures/sample_docs/microservices.md",
          "tests/fixtures/scale_docs/design-patterns-structural.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about best coffee shops in London.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 1533,
        "worker_latency_ms": 10004.101499915123,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "gardening tips for summer",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/network-security-hardening.md",
          "tests/fixtures/scale_docs/design-patterns-behavioral.md",
          "tests/fixtures/scale_docs/auto-scaling.md",
          "tests/fixtures/scale_docs/migration-strategies.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 3190,
        "worker_latency_ms": 15226.421666913666,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "how to repair a flat tire",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/database-backup-recovery.md",
          "tests/fixtures/scale_docs/linux-filesystem.md",
          "tests/fixtures/scale_docs/elasticsearch-basics.md",
          "tests/fixtures/scale_docs/cloud-design-patterns.md",
          "tests/fixtures/scale_docs/database-backup-recovery.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about how to repair a flat tire. It discusses database backup and recovery strategies, Linux filesystem hierarchy, Elasticsearch shard rebalancing, and cloud design patterns.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 4365,
        "worker_latency_ms": 19039.50208297465,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "history of the internet",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/http2-http3.md",
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md",
          "tests/fixtures/scale_docs/data-warehouse-modeling.md",
          "tests/fixtures/scale_docs/cloud-networking.md",
          "tests/fixtures/scale_docs/tcp-ip-fundamentals.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about the history of the internet. It focuses on technical details of HTTP/2 and HTTP/3 protocols.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 6880,
        "worker_latency_ms": 19175.736415898427,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "space travel in the future",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/concurrency-patterns.md",
          "tests/fixtures/scale_docs/infrastructure-as-code.md",
          "tests/fixtures/scale_docs/incident-response.md",
          "tests/fixtures/scale_docs/linux-memory-management.md",
          "tests/fixtures/scale_docs/multi-region-deployment.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain any information about space travel in the future. It discusses technical guides on concurrency patterns and constructs, Terraform for Infrastructure as Code, incident response procedures, and Linux memory management.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 3217,
        "worker_latency_ms": 18254.236791981384,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "the art of war summary",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/incident-response.md",
          "tests/fixtures/scale_docs/penetration-testing-methodology.md",
          "tests/fixtures/scale_docs/cloud-storage-options.md",
          "tests/fixtures/scale_docs/incident-response.md",
          "tests/fixtures/scale_docs/design-patterns-creational.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about \"the art of war summary\". It discusses incident response procedures, postmortems, runbooks, and communication templates.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 4230,
        "worker_latency_ms": 18077.960500027984,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "meditation techniques",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/stream-processing.md",
          "tests/fixtures/sample_docs/semantic-search.md",
          "tests/fixtures/scale_docs/stream-processing.md",
          "tests/fixtures/scale_docs/design-patterns-behavioral.md",
          "tests/fixtures/scale_docs/nosql-patterns.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 3595,
        "worker_latency_ms": 17241.925250040367,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "learning a new language fast",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/testing-strategies.md",
          "tests/fixtures/scale_docs/data-serialization.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/sample_docs/semantic-search.md",
          "tests/fixtures/scale_docs/container-security.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 1741,
        "worker_latency_ms": 10133.328457945026,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "climate change impacts",
        "category": "adversarial",
        "difficulty": "medium",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/incident-response.md",
          "tests/fixtures/scale_docs/linux-permissions.md",
          "tests/fixtures/scale_docs/database-replication.md",
          "tests/fixtures/scale_docs/zero-trust-architecture.md",
          "tests/fixtures/scale_docs/chaos-engineering.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 1391,
        "worker_latency_ms": 7111.210249946453,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "renewable energy sources",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/container-fundamentals.md",
          "tests/fixtures/scale_docs/owasp-top-10.md",
          "tests/fixtures/scale_docs/data-quality.md",
          "tests/fixtures/scale_docs/serverless-architecture.md",
          "tests/fixtures/scale_docs/disaster-recovery.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain any information about renewable energy sources.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 4757,
        "worker_latency_ms": 16644.587957998738,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "healthy eating habits",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/load-balancing.md",
          "tests/fixtures/sample_docs/database-design.md",
          "tests/fixtures/scale_docs/connection-pooling.md",
          "tests/fixtures/scale_docs/kubernetes-architecture.md",
          "tests/fixtures/scale_docs/chaos-engineering.md"
        ],
        "worker_answer": "I don't know.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 2054,
        "worker_latency_ms": 11669.174292008393,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "travel tips for Japan",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/disaster-recovery.md",
          "tests/fixtures/scale_docs/sql-query-optimization.md",
          "tests/fixtures/scale_docs/event-driven-architecture.md",
          "tests/fixtures/scale_docs/connection-pooling.md",
          "tests/fixtures/scale_docs/websocket-protocol.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about travel tips for Japan.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 4842,
        "worker_latency_ms": 17697.2931670025,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      },
      {
        "competitor": "candlekeep-hybrid",
        "query": "basics of photography",
        "category": "adversarial",
        "difficulty": "easy",
        "retrieved_sources": [
          "tests/fixtures/scale_docs/container-fundamentals.md",
          "tests/fixtures/scale_docs/container-fundamentals.md"
        ],
        "worker_answer": "I don't know. The provided context does not contain information about the basics of photography. It focuses on container technologies and Docker.",
        "reference_answer": "The provided context does not contain information to answer this question.",
        "score": {
          "correctness": 3,
          "completeness": 3,
          "hallucination": 1,
          "combined": 1.0
        },
        "tokens_in_context": 3073,
        "worker_latency_ms": 17314.37616609037,
        "judge_raw_response": "{\"correctness\": 3, \"completeness\": 3, \"hallucination\": 1}"
      }
    ]
  }
}