# RAG Evaluation Best Practices
Retrieval-augmented generation is now a default pattern for enterprise AI apps, but many teams still judge quality by reading a few sample answers. That is not enough for production. A reliable RAG system needs repeatable evaluation across retrieval, generation, and user experience.
## Start With Real Questions
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Build an evaluation set from support tickets, search logs, sales calls, product documentation, and internal knowledge base queries. Synthetic questions can help fill gaps, but real user language reveals messy phrasing, missing context, and ambiguous intent.
## Measure Retrieval Separately
Before judging the final answer, check whether the retriever found the right evidence. Track:
- **Recall**: did the system retrieve the documents needed to answer?
- **Precision**: were the retrieved chunks actually relevant?
- **Rank quality**: did the best evidence appear near the top?
- **Coverage**: are important topics underrepresented in the index?
## Evaluate Grounded Answers
A good RAG answer should be useful and grounded in the supplied context. Score whether the response answers the question, cites the correct source, avoids unsupported claims, and admits when the knowledge base does not contain enough information.
## Tune Chunking and Metadata
Chunk size, overlap, document structure, and metadata filters can matter more than the embedding model. Preserve headings, product names, dates, permissions, and source URLs so retrieved context is easier for the model to use.
## Add Regression Tests
Every update to prompts, indexes, embeddings, or rerankers can change behaviour. Keep a small high-value regression suite that runs in CI so quality does not drift after seemingly harmless changes.
## Conclusion
RAG quality improves fastest when teams measure each stage independently. Retrieval, reranking, prompting, and answer generation all need their own feedback loops.
## Measure Retrieval Separately
Before judging the final answer, check whether the retriever found the right evidence. Track:
- **Recall**: did the system retrieve the documents needed to answer?
- **Precision**: were the retrieved chunks actually relevant?
- **Rank quality**: did the best evidence appear near the top?
- **Coverage**: are important topics underrepresented in the index?
## Evaluate Grounded Answers
A good RAG answer should be useful and grounded in the supplied context. Score whether the response answers the question, cites the correct source, avoids unsupported claims, and admits when the knowledge base does not contain enough information.
## Tune Chunking and Metadata
Chunk size, overlap, document structure, and metadata filters can matter more than the embedding model. Preserve headings, product names, dates, permissions, and source URLs so retrieved context is easier for the model to use.
## Add Regression Tests
Every update to prompts, indexes, embeddings, or rerankers can change behaviour. Keep a small high-value regression suite that runs in CI so quality does not drift after seemingly harmless changes.
## Conclusion
RAG quality improves fastest when teams measure each stage independently. Retrieval, reranking, prompting, and answer generation all need their own feedback loops.
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