What is Recall and precision?
Verified 5 August 2026
The two trade against each other. Returning more results raises recall and lowers precision; returning fewer does the opposite. Which one to optimise depends entirely on who is reading the results.
Human search engines optimise recall and rank: a person skims and discards, so a hundred results with the good one first is a fine outcome. Agents cannot do that — every irrelevant result costs context and reasoning tokens. For agent retrieval, precision dominates.
That inversion is the argument for admission control: move quality from query-time ranking to index-time admission, and return fewer, better things.
How unlob handles this: How it works
Common questions
What is Recall and precision?
Recall is the share of relevant documents a search returns; precision is the share of returned documents that are relevant.
How does Recall and precision work in practice?
Human search engines optimise recall and rank: a person skims and discards, so a hundred results with the good one first is a fine outcome. Agents cannot do that — every irrelevant result costs context and reasoning tokens. For agent retrieval, precision dominates.
See it working
The free tier is 10,000 requests a month with no card — enough to test any of this against your own queries.