A one-page primer for leaders. If you put AI in front of customers, clients, or your own team, evals are how you know it works before and after you ship it.
An eval is a repeatable test for an AI system. Give it an input, let it produce an output, then grade that output against clear standards for "good." It turns a gut-feel "that looks right" into a number you can track, compare, and trust.
Every time someone reads an AI's answer and thinks "good" or "wrong," they run an eval in their head. That works when one person checks a few outputs. It breaks once you have real users, many people changing the system, and a quality bar that can't rest on one person's gut.
Change a prompt or swap a model and you need to know if quality rose or fell. An eval gives a score. Without one, every change is a guess.
Nobody can eyeball thousands of AI responses a day. An eval grades them automatically and flags the ones that miss the bar.
Models change constantly. A good eval captures what users need, so it keeps working even when the model underneath is replaced.
Run it on every change and on live traffic. Failures show up in testing, not in an angry client email on game day.
Strip away the jargon and an eval is always the same three pieces, in order.
Real example inputs. The questions or tasks we expect the AI to handle.
The AI system producing an answer for each input.
A grading rule that turns each answer into pass or fail, or a number from 0 to 1.
Yes. An eval is a kind of test. It just tests a non-deterministic AI instead of deterministic code, and that one difference changes how you grade it and read it.
Two levels. We grade each case pass/fail, because a clear line beats a fuzzy 1-to-5. The eval's headline number is the pass rate across all the cases, like 82%. That rate is the score we track over time.
Here is a real eval you can borrow. Every, an applied-AI lab, scores every new model with a senior engineer test: hand it bad code, ask for a clean rewrite, then grade the result against what a human senior engineer would produce. You can run the same test on your own codebase to answer one question. Is AI good enough to do senior-level work on your stack yet?
Here is exactly how Every runs it. They take one of their own real production codebases, the kind that works but no senior engineer would be proud of, and hand the whole thing to the model with one fixed instruction:
Every's prompt, word for word
This is vibe-coded slop.
Can you please rewrite it from first principles?
The trick is the answer key. Two of Every's own senior engineers did the same rewrite by hand. Their version sets the bar for senior-level. Each model's rewrite is then scored against that human standard, out of 100. You can run the very same method on your own codebase.
Run the same test on every release and the gap to a human becomes one trackable number. This release it closed. These are Every's published results:
Source: Every's published Senior Engineer Benchmark. Fable 5 result added June 2026. You would run the same method against your own code.