# RAG System Technical Report

## Overview

Retrieval-Augmented Generation (RAG) improves large language model performance on domain knowledge by combining retrieval with generation. A typical pipeline includes: document chunking, vectorization, retrieval, context assembly, and generation.

## Failure Modes

- **Retrieval miss**: Relevant chunks are not recalled, leading to hallucination or omission.
- **Context overflow**: Exceeding the context window causes truncation or noise.
- **Ranking bias**: The most relevant chunks are not ranked at the top, degrading generation quality.

## Practical Recommendations

- Clearly map retrieval metrics (Recall@K, MRR) to business metrics (satisfaction, conversion).
- For sensitive scenarios, conduct manual sampling and A/B testing.
