Evals

An eval (short for evaluation) is just looking at AI output and deciding if it meets your standard, made repeatable instead of a one-off gut check.

Here's why you need it. One demo is a story. A single run can look perfect or broken by pure chance, because AI output is non-deterministic: the same prompt can give a different answer each time. An eval measures across a set of cases, so "3 of 5 passed" becomes a 60% pass rate you can compare against.

You build one with four pieces. A dataset: 20 to 50 realistic test cases, which is plenty to start. A harness: the setup around the model, meaning the prompt, tools, files, and rules. A grader: how you decide each result passed, whether by code, by an AI judge, or by a human. And metrics: the numbers you act on, like pass rate or top failure theme.

One tip on graders. Prefer the most objective one that still measures what you care about: exact match or a unit test beats an AI judge, and an AI judge (the technical name is LLM-as-judge) beats nothing. If a person or an AI is grading open-ended work, first check the grader agrees with a standard you trust before you believe its scores.

Build an eval whenever output quality matters and you can't just eyeball it once: before you ship a prompt change, before you switch models, or any time the AI is inconsistent and you need to know how much. Start small, five to fifty cases with written pass/fail notes, and add sophistication only when the simple version stops answering your question. Don't over-engineer one for a throwaway task.