Evaluating RAG systems: Separating evidence from answers

This article outlines a method for evaluating retrieval-augmented generation (RAG) systems by separating the assessment of retrieved evidence from the generated answers. It emphasizes the importance of creating a comprehensive evaluation set that includes expected evidence, unanswerable questions, and adversarial cases. The guide details how to calculate retrieval metrics like Recall@k and Mean Reciprocal Rank, and stresses that a successful retrieval is only a prerequisite, not a guarantee, of a correct and supported answer. Finally, it advises on careful record-keeping, avoiding evaluation leakage, and setting release requirements based on product risk. AI

IMPACT Provides a structured approach to assessing RAG system performance, crucial for developers aiming for reliable and accurate AI applications.

RANK_REASON The item describes a methodology for evaluating a specific type of AI system, akin to a research paper or technical guide. [lever_c_demoted from research: ic=1 ai=1.0]

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