Recall is part of AI evaluation methodology. Practitioners apply it to quantify quality, compare variants, detect failures, and build evidence for release decisions without relying on anecdotes alone.
AI Glossary
Recall
Coverage in development
Recall is part of AI evaluation methodology. Practitioners apply it to quantify quality, compare variants, detect failures, and build evidence for release decisions without relying on anecdotes alone.
Definition
Plain English explanation
Recall helps teams decide whether a model is good enough to ship.
Related glossary terms
Last reviewed
Sources
- NIST AI Risk Management Framework — risk vocabulary context for AI systems
- Brel Digital company and technology profiles — applied usage evidence where tagged
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