An AI eval is a repeatable test that scores an AI system's output against a standard of what good looks like. Instead of eyeballing a few answers, you run the model against a set of real examples, grade each one by a clear rubric, and get a number you can compare across versions.
If you've written software tests, the instinct is familiar. The difference is that AI output varies, so an eval doesn't assert one exact answer. It measures how close the output gets to your standard, across many cases, and reports the result as a score you can track over time.
The three parts of an eval
Every eval, however simple, has the same three pieces:
- A dataset. A set of real inputs to run the AI against: actual user questions, real documents, the edge cases from your logs.
- A rubric. A clear definition of what a good output looks like for this task, specific enough that two people would grade the same output the same way.
- A scoring method. How you grade each output against the rubric: deterministic code checks, human review, or a second model acting as judge.
Take any of those away and you don't have an eval. You have a demo, an opinion, or a number nobody can interpret.
Why evals matter
AI features fail quietly. A model update, a reworded prompt, or a new data source can degrade quality without throwing an error. Nobody notices until a user hits the bad output. An eval is the standing check that catches the regression before it ships.
It also changes how a team argues. "This version feels better" is an opinion two smart people can hold opposite versions of. "This version scores higher on the cases that generate support tickets" is a fact you can act on. Evals move the conversation from taste to evidence.
A tiny example
Say you're building an AI that drafts support replies. Your dataset is 30 real customer messages. Your rubric: the reply answers the question, cites the right policy, and stays under 150 words. Your scoring: a code check for length, plus a human (or a second model) grading accuracy and tone.
Run it on today's prompt. You get a score. Change the prompt, run it again, and you know within minutes whether you made things better or worse. That is the whole idea.
Where to go next
Once you understand what an eval is, the next question is how to build one well. I walk through that in how to design AI evals, and my free AI Eval Builder helps you draft your first one. From there, eval-driven development shows how to make evals part of your build loop, and how to test for AI hallucinations covers the failure mode evals catch most often. If you'd rather have help standing this up, AI adoption consulting is how I work with teams on exactly this.
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