Jargonautica
Retrieval and Knowledge

Retrieval Metrics

Also called: Recall at k, NDCG

How to tell whether your search is any good, separately from whether the final answer is good. If the right document was never retrieved, no amount of prompt engineering will save the answer, so measuring the retrieval step on its own is the first diagnostic anyone should build.

Technical view

Recall at k is the primary measure for retrieval feeding generation, since generation cannot use what was not retrieved. Precision at k, mean reciprocal rank and normalised discounted cumulative gain capture ranking quality where position matters. Building the ground truth requires labelled query-document relevance, which can be bootstrapped by generating questions from known passages. Report retrieval and generation metrics separately or failures cannot be attributed.


Part of AI Terminology Index, section: Retrieval and Knowledge.

How it connects

Retrieval Metrics links to 10 other terms. The faint lines are links between those terms. See it in the full graph.

Retrieval Augmented GenerationRetrieval Augmented…Hybrid SearchHybrid SearchInformation RetrievalInformation Retriev…BM25 and Sparse RetrievalBM25 and Sparse Ret…Benchmarks and EvalsBenchmarks and EvalsGolden DatasetGolden DatasetLearning to RankLearning to RankQuery RewritingQuery RewritingContextual RetrievalContextual RetrievalApproximate Nearest Neighbour SearchApproximate Nearest…Retrieval Metrics