AI Evals
Comparing model or workflow outputs using explicit cases and criteria.
Same requests
□ Correctness□ Format□ Boundary cases
In practice
Evaluate a revised project assistant on the same redacted requests, checking evidence, format and difficult cases. Keep outputs and versions.
What to check
Define usability first, then choose automated checks or human review. Include ordinary requests and observed failures.
Common confusion
One impressive response is not overall reliability. Model graders can be wrong; keep evaluation cases separate from tuning examples.
Tell your agent
Prepare redacted representative requests and known failures with clear correctness, format and refusal criteria. Preserve model and prompt versions plus raw outputs, distinguishing automated judgments from human review.