They’re how you move from “this feels fine” to “this passes the standard we agreed on.” Testing does this for software; evals do it for AI behavior. A code test might check that a function returns the right answer. An AI eval might check whether a workflow extracts the right fields from ten sample transcripts, refuses unsafe requests, or produces the expected structure.
Think about a scorecard you use every time you review a client deliverable. You don’t ask “do I like it?” in a vague way. You check whether the numbers tie, the client’s name is right, the source is cited, and the format matches the firm standard. The scorecard makes quality less dependent on mood.
How it shows up
Evals aren’t only for big AI labs. “Run this skill on these five known examples and compare the output to the expected result” is an eval. They often sit next to guardrails: a guardrail says what shouldn’t happen, an eval checks whether the system actually behaves that way. Testing also guards against fake progress, since a new prompt may shine on one example and fail on five others, or a new model may be smarter on reasoning tasks but worse at your format. This is why cross-model review helps but isn’t enough: a second model finds issues once, a test repeats the check on every CI/CD run.
Why you care
For a nontechnical operator, the question isn’t “do we have a benchmark suite?” It’s “what examples would prove this works?” Save them, run the workflow against them, keep the expected answer, and run them again when the system changes. AI work doesn’t improve just because the output sounds more confident. Evals are how you know it actually did.