Eval (evaluation)

A systematic test that measures how well an AI system performs on a defined task, using scored examples rather than subjective impressions.

Asking an AI a few questions and eyeballing the answers tells you very little about reliability. An eval replaces that intuition with structure: a set of test cases with known-good answers, a scoring function that compares model output against those answers, and a pass/fail threshold. You might eval a customer-support agent on 200 past tickets where you already know the correct resolution, scoring it on accuracy, tone, and whether it escalated when it should have. The eval runs automatically, so you can re-run it after every prompt change, model upgrade, or context modification and see exactly what improved, what regressed, and what stayed the same.

Builder example

Without evals, every change to your AI system is a guess. You update a prompt, it feels better on two examples, and you ship it. Evals replace that feeling with evidence. They are the only reliable way to compare model versions, measure the impact of prompt changes, and catch regressions before users do. Teams that skip evals optimize for demo quality; teams that invest in evals optimize for production quality.

Common confusion: An eval is not a benchmark. Benchmarks measure general model capability across standardized tasks. Evals measure your specific system's performance on your specific task with your specific data. A model that scores well on a public benchmark can still fail your eval if your task has domain-specific requirements the benchmark does not cover.