Evaluation and Quality Assurance
Building a RAG pipeline is easy. Building a RAG pipeline that reliably delivers correct answers is hard. Systematic evaluation is the difference between prototype and production.
The Three Quality Dimensions
1. Retrieval Quality
Does the pipeline find the right chunks?
- Precision@K: How many of the top-K chunks are actually relevant?
- Recall@K: How many relevant chunks were found?
- MRR (Mean Reciprocal Rank): How high is the first relevant chunk ranked?
2. Generation Quality
Does the LLM generate correct answers from the chunks?
- Faithfulness: Is the answer supported by the sources? (No hallucinations)
- Answer Relevancy: Does the answer actually address the question asked?
- Completeness: Does the answer contain all relevant information?
3. End-to-End Quality
How well does the overall system perform?
- Correctness: Is the final answer right?
- Latency: How fast does the answer come?
- User Satisfaction: Do real users rate answers positively?
RAGAS Framework
RAGAS (Retrieval Augmented Generation Assessment) is the de facto standard for RAG evaluation:
| Metric | Measures | Range |
|---|
| Faithfulness | Freedom from hallucinations | 0–1 (higher = better) |
| Answer Relevancy | Answer relevance | 0–1 |
| Context Precision | Retrieval quality | 0–1 |
| Context Recall | Retrieval completeness | 0–1 |
Evaluation Workflow
- Create Golden Dataset: 50–100 question-answer pairs with expected sources
- Automated Tests: Calculate RAGAS metrics after every pipeline change
- Human Evaluation: Have domain experts evaluate samples
- A/B Testing: Compare different configurations (chunk size, reranker, prompts)
- Production Monitoring: Track user feedback, latency, error rate
Common Problems and Fixes
| Problem | Cause | Fix |
|---|
| Wrong answers | Irrelevant chunks | Reranking, better chunking |
| "I don't know" | Relevant docs missing | Expand document base |
| Hallucinations | Weak prompt | Tighten system prompt |
| Slow | Too many chunks | Reduce top-K, caching |
Practical tip: Invest 30% of your RAG development time in evaluation. A golden dataset with 50 questions that you run after every change saves weeks of debugging.