How do you measure retrieval quality for an agent’s RAG pipeline?

Question

Accepted Answer

Measuring retrieval quality in an agent’s RAG pipeline is critical for ensuring the agent receives the most relevant and comprehensive context for its tasks. This is primarily assessed using metrics like precision, which indicates the proportion of retrieved documents that are truly relevant, and recall, determining if all pertinent information was found. Evaluation often involves human annotation to judge document relevance against specific queries, or increasingly, LLM-as-a-judge frameworks for automated relevance scoring at scale. For ranked retrieval lists, metrics such as Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG) are crucial, as they account for the position and graded relevance of documents. Additionally, the hit rate or success rate provides a straightforward measure of whether at least one relevant document appeared within the top-K retrieved results. Ultimately, effective retrieval directly contributes to the agent's capacity to generate accurate and grounded responses, validating the end-to-end pipeline's performance.