Tests are how you change code without fear. Evals are how you change prompts, models, and agents without fear. Without them, every tweak is a gamble you cannot measure.
TL;DR
- Evals are to AI systems what tests are to code: a safety net for change.
- Build a versioned suite of representative inputs with a way to judge outputs.
- Run it on every prompt change, model upgrade, and agent tweak.
- Grow it from real production failures.
Why evals are non-negotiable
Change a prompt and what happens? Without evals, you find out in production. AI systems are unusually fragile to change — a small prompt edit, a model version bump, a context tweak can shift behavior in ways no one intended. Evals turn "I think this is better" into "this scores higher on 50 real cases," which is the difference between engineering and vibes.
Anatomy of an eval suite
Three ingredients:
- A dataset of representative inputs — real examples from your domain, including the edge cases and known-hard cases.
- A judgment method per case: exact match, a rubric, structural checks, or LLM-as-judge with a clear scoring guide.
- A runner that executes your system against the dataset and reports a score.
For each example in the eval set:
output = system(example.input)
score = judge(output, example.expected)
Report: aggregate score, per-category breakdown, regressions vs. last run
Choosing a judgment method
- Exact / structural match — best when there is a single correct answer or a required shape (valid JSON, specific field).
- Rubric scoring — for open-ended outputs, score against explicit criteria (accuracy, completeness, tone).
- LLM-as-judge — scalable for subjective quality, but the judge needs its own clear instructions and spot-checking, or you have just moved the trust problem.
The judge is part of your system. A sloppy judge gives you confident, meaningless scores — the worst kind.
Run them like tests
Evals earn their keep by running automatically at the moments behavior can change:
- On every prompt change — did this edit help the target case without hurting others?
- On every model upgrade — a "better" model can silently regress your specific task.
- On agent changes — new tools, new context, new memory all shift behavior.
- In CI where practical, so regressions are caught before merge, not after deploy.
Grow the suite from real failures
The most valuable eval cases come from production. Every time the system fails a user, capture the input and add it — with the correct expected behavior — to the suite. Over time your evals become an accumulated, distilled record of every way your system has been wrong, and a wall against repeating it. This is the same loop as good observability: real failures in, regressions out.
The takeaway
Treat evals as a first-class artifact — versioned, maintained, and run constantly — exactly like your test suite. They are what let you upgrade models, refine prompts, and evolve agents with confidence instead of crossed fingers. In a world where the model under you changes every few weeks, the eval suite is the stable ground you stand on.