Recall at K
Also called Recall@K
Of all the right answers out there, how many showed up in your top K results.
Think of it like
Casting a net and asking: of all the fish in the pond you wanted, how many did you actually pull up?
Example
If a query has 4 relevant docs and 3 appear in the top 10, recall@10 is 0.75.
How it actually works
Recall@K measures coverage: did retrieval surface the relevant items within the first K? It matters most in RAG, where a missed passage means the model literally cannot answer. High recall usually comes at the cost of precision — a bigger K catches more truth but also more noise, which then leans on reranking to sort out.
For product teams
If recall is low, no amount of prompt tuning helps — the answer never reached the model.
For engineers
|relevant ∩ top-K| / |relevant|; the recall/precision trade-off is set by K and the retriever.
Related
- Precision at K — The precision counterpart.
- Retrieval Quality — A core measure of retrieval quality.
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