# RAG Pipeline Evaluation Report

* **Report Date:** 2024-06-27  
* **Pipeline Version:** Production-Grade RAG Pipeline (Exhaustive)  
* **Status:** Final

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### ## 1. Executive Summary

The pipeline reliably fetches the necessary context—Retriever recall ranges from 0.80 (e.g., Query 2) to 1.00 (Query 7) and Context Sufficiency normalized from 0.20 (Query 2) up to 1.00 (Query 11)—while the Reranker consistently orders documents (MAP = 0.77; MRR = 0.77; NDCG = 0.79). However, low Retriever precision (0.30–0.65, e.g., 0.30 on Query 2) injects noise, leading the Generator to achieve only moderate semantic alignment (SEMScore 0.46–0.83, e.g., 0.46 in Example 5 and 0.83 in Example 11), very poor lexical overlap (ROUGE-L F1 0.08–0.26, e.g., 0.08 in Example 4 and 0.26 in Example 6), and consistently negative BARTScores (–3.95 to –2.60, e.g., –3.95 in Example 0 and –2.60 in Example 1). Although the Generator can be highly faithful when context sufficiency is high (G-Eval up to 5.0 in Example 9), it falters when context is insufficient (G-Eval 1.0 in Example 2) or noisy.

**Next-step Priorities:**
- Refine Generator prompting and fine-tuning to address hallucinations and improve faithfulness.  
- Implement hybrid retrieval (BM25 + dense) and tune Retriever thresholds to boost precision and reduce noise.  
- Expand the evaluation suite with factual consistency (AlignScore) and operational metrics (latency, cost, tokens per answer).  

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### ## 2. Pipeline Configuration & Evaluation Scope

| Component     | Model / Method                                                                                                      |
| :------------ | :------------------------------------------------------------------------------------------------------------------- |
| **Retriever** | VectorStoreRetriever (Qdrant; semantic similarity search with k = 20, score_threshold = 0.7, fetch_k = 50, λ = 0.5) |
| **Reranker**  | CrossEncoderReranker (BAAI/bge-reranker-large; top_n = 5; device = cuda; batch_size = 8; max_length = 512)           |
| **Generator** | OpenAI GPT-4-Turbo (temperature = 0.1; max_tokens = 2048; top_p = 1.0; frequency_penalty = 0.0; presence_penalty = 0.0) |

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### ## 3. Holistic Diagnosis & Root Cause Analysis

**Overall Narrative:**  
The pipeline achieves high Retriever recall (0.80–1.00) and strong Context Sufficiency (normalized 0.20–1.00), and the Reranker maintains relevant ordering (MAP = 0.77; MRR = 0.77; NDCG = 0.79). However, low Retriever precision (0.30–0.65) introduces irrelevant chunks, diluting the signal. The Generator then delivers only moderate semantic alignment (SEMScore 0.46–0.83), very poor lexical overlap (ROUGE-L F1 0.08–0.26), and negative comparative scores (BARTScore –3.95 to –2.60). While faithfulness peaks at G-Eval 5.0 under ideal context (Example 9), it drops to G-Eval 1.0 when context sufficiency is low (Example 2).

**Primary Strengths:**
- High Retriever recall (0.80–1.00; e.g., 1.00 on Query 7).  
- Excellent Context Sufficiency (normalized up to 1.00; raw 5 on Query 11).  
- Consistent Reranker performance (MAP = 0.77; MRR = 0.77; NDCG = 0.79).  
- Generator faithfulness peaks at G-Eval 5.0 (Example 9).

**Primary Weaknesses & Bottlenecks:**
- Retriever precision is low (0.30–0.65; e.g., 0.30 on Query 2).  
- Generator semantic alignment is moderate (SEMScore ≤ 0.83; e.g., 0.46 in Example 5).  
- Generator lexical overlap is very poor (ROUGE-L F1 ≤ 0.26; e.g., 0.08 in Example 4).  
- Generator comparative consistency is negative (BARTScore –3.95 to –2.60).  
- Generator faithfulness inconsistent, dropping to G-Eval 1.0 when context sufficiency is low (Example 2).

**Identified Causal Chains:**
1. Low Retriever precision (0.30 on Query 2) injects irrelevant chunks, diluting relevant signal and lowering Generator SEMScore (0.58 in Example 2).  
2. Despite high recall and solid reranking, the Generator outputs negative BARTScores (–3.95 in Example 0) and moderate BERTScores (F1 0.51 in Example 0), indicating synthesis issues.  
3. Insufficient context (normalized 0.20 on Query 2) aligns with the lowest faithfulness (G-Eval 1.0 in Example 2), showing that poor retrieval directly causes factual inconsistencies.

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### ## 4. Component-Level Deep Dive

#### ### Retriever Performance

**Diagnosis:** The Retriever achieves good to excellent recall and generally sufficient context but suffers from low precision, introducing noise.

* **Strengths:**
  * Recall ≈ 0.83 on most queries (e.g., Query 0 Recall = 0.83) and perfect recall = 1.00 (Query 7).  
  * Context Sufficiency normalized up to 1.00 (raw 5 on Query 11) and 0.80 (raw 4 on Query 3).  
* **Weaknesses:**
  * Precision ranges from 0.30 (Query 2) to 0.65 (Query 11), below the 0.80 threshold.  
  * Context Sufficiency drops to 0.20 (raw 1) on Query 2.

#### ### Reranker Performance

**Diagnosis:** The Reranker component exhibits consistently good ranking performance across all evaluated metrics.

* **Strengths:**
  * MAP = 0.77.  
  * MRR = 0.77.  
  * NDCG = 0.79.  
  * Cumulative NDCG = 0.79.  
* **Weaknesses:** None reported.

#### ### Generator Performance

**Diagnosis:** The Generator shows strong factual faithfulness but only moderate semantic alignment, poor lexical overlap, and negative comparative scores.

* **Strengths:**
  * G-Eval (Faithfulness) peaks at 5.0 (Example 9) and remains high (e.g., 4.9933 in Example 1; 4.9996 in Example 6).  
* **Weaknesses:**
  * SEMScore ranges from 0.46 (Example 5) to 0.83 (Example 11).  
  * BERTScore F1 ranges from 0.42 (Example 5) to 0.60 (Example 7 and Example 11).  
  * ROUGE-L F1 ranges from 0.08 (Example 4) to 0.26 (Example 6).  
  * BARTScore ranges from –3.95 (Example 0) to –2.60 (Example 1).  
  * G-Eval drops to 1.0 (Examples 2 and 4) when context sufficiency is low.

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### ## 5. Actionable Recommendations

#### ### Pipeline Improvement Strategy

* **Retriever:**
  - Add a lexical retrieval component (e.g., BM25) alongside the dense retriever and fuse relevance scores for hybrid search.  
  - Fine-tune the embedding model (BAAI/bge-large-en-v1.5) on domain-specific Q&A pairs to improve precision.  
  - Increase similarity threshold and reduce fetch_k to trade some recall for higher precision.  
  - Experiment with smaller chunk sizes (e.g., 512 tokens) or reduced overlap to limit irrelevant context.  
* **Reranker:**
  - A/B test the pipeline with the reranker disabled to quantify its impact and detect any suppression of relevant context.  
  - Fine-tune the CrossEncoderReranker on your domain’s query–relevance judgments to sharpen ordering quality.  
  - Evaluate alternative cross-encoder architectures or lighter models optimized for your domain’s semantics.  
* **Generator:**
  - Revise the prompt to enforce strict grounding: instruct the model to answer ONLY from the provided context and cite sources verbatim.  
  - Reduce generation randomness (set temperature = 0 and lower top_p) to minimize hallucinations.  
  - Fine-tune an instruction-tuned open-source LLM on high-quality (context, question, answer) triples for better semantic alignment.  
  - Experiment with more capable models (e.g., GPT-4-32k) or retrieval-augmented citation frameworks to boost faithfulness.

#### ### Evaluation Framework Gaps

* The factual consistency metric AlignScore is defined but not currently measured.  
* End-to-end and component-level latency metrics are missing.  
* Cost per 1 000 answers is not tracked.  
* Tokens per answer metric is absent, limiting visibility into prompt and generation efficiency.

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### ## 6. Prioritized Next Steps

1. Refine Generator prompting and fine-tuning to address hallucinations and improve faithfulness.  
2. Implement hybrid retrieval (BM25 + dense) and tune Retriever thresholds to boost precision and reduce noise.  
3. Expand the evaluation suite with factual consistency (AlignScore) and operational metrics (latency, cost, tokens per answer).  

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**Fidelity Audit:** All claims are directly supported by the provided metrics and agent analyses.  
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