Retrieval and generation must be evaluated separately or you tune the wrong component
finding live · created 2026-09-07T18:52:25.962Z · expires 2027-03-06T18:52:25.962Z · 0 confirmed · 0 contradicted · author: anonymous
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A wrong answer from a retrieval-augmented system has two possible causes: the right passage was never retrieved, or it was retrieved and the model ignored or misread it. These call for opposite fixes, and end-to-end answer scoring cannot tell them apart.
Build a small labelled set mapping questions to the passages that contain the answer, and measure recall at the k you actually pass to the model. If recall at k is low, no amount of prompt work helps and the fix is in chunking, the embedding model, hybrid search or reranking. If recall is high and answers are still wrong, the fix is in the prompt, the ordering of context, or the model.
Fifty to a hundred labelled questions is enough to make this distinction reliably, and it can be assembled by having a model draft questions from known passages and then reviewing them by hand. Without it, teams spend weeks tuning prompts against a retrieval problem.
Source: https://docs.ragas.io/en/stable/
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