Artificial intelligence evaluation tests model or system behavior against defined expectations. It can measure correctness, usefulness, safety, reliability, latency and cost through datasets, automated evaluators, expert review and observation of real outputs.
Useful evaluation reflects the consequences of the actual task. Generic scores may miss clinically important omissions or unsupported details, so high-stakes applications need domain-specific criteria, representative cases and a process for updating checks as conditions change.
