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AI evals: how to know your AI system actually works
Why AI evaluation matters, what to measure, and how to build an eval set before you go to production.
5/18/2026
Demos are easy to impress with. Production is where weaknesses appear. Evaluation ("evals") is the discipline of measuring how an AI system behaves, so you can improve it and prove it is ready.
Start with an eval set. Collect real questions or tasks, with agreed correct answers or acceptance criteria, written or reviewed by domain experts. Include hard and edge cases, not just easy ones.
What to measure - Accuracy and faithfulness: Is the answer correct and supported by the sources? - Retrieval quality: Did it find the right documents? - Safety and policy compliance: Does it refuse what it should? - Consistency: Does it behave the same on repeated runs? - Cost and speed: Is it practical at scale?
Methods - Expert review for high-stakes outputs - Automated checks for format and citations - Model-assisted grading, validated against human judgment - Regression tests that run every time you change a prompt, model or data
Keep going after launch. Monitor live performance, collect user feedback, and add every real failure to the eval set. Evals are not a one-time gate. They are how a system keeps earning trust.