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What Are AI Evals? (Report Cards for Your AI)
TL;DR: Evals are tests that grade your AI. They tell you if it's right, safe, and reliable — before real users find the problems for you.
Explain it like I'm 5
Before a chef sells a new dish, they taste it — a lot. Evals are the taste tests for your AI. You try it on many examples and check: is it good? Is it right? Only then do you serve it to customers.
What are evals?
An eval is a set of test questions with known good answers. You run your AI on all of them and score how well it did. It turns "it feels okay" into a real number you can trust and improve.
Why can't I just eyeball it?
Because a couple of good answers in a demo don't mean it works for everyone. AI is a bit random and edge cases hide. Evals catch the failures you'd never notice by hand — before your users do.
What a simple eval looks like
- Collect real examples of questions users will ask.
- Write down the correct or ideal answer for each.
- Run your AI on all of them.
- Score the results — by rules, by another AI, or by a human.
- Change your prompt or setup and run again to see if the score went up.
Why evals separate pros from amateurs
Anyone can get one good demo. Professionals prove their AI works with numbers, then improve it on purpose. Evals are how you go from "looks cool" to "safe to ship," and how you keep it good over time.
Frequently asked questions
Do I need evals for a small project?
Even a tiny set of 10–20 test cases helps a lot. It catches obvious breakages and gives you a baseline to improve against.
Can AI grade its own tests?
Yes — using one AI to score another's answers ("LLM as a judge") is common and useful, especially combined with some human checks.
How often should I run evals?
Every time you change your prompt, model, or setup. Evals tell you instantly whether your change made things better or worse.
Want to know exactly where you stand on the road from using AI to building it? Take the free 3-minute AI Builder Scorecard and get your single biggest gap.