Why AI Products Need Evals
Understand why 'it looks good in the demo' is not a quality process, and what an eval set actually is.
The gap between demo and production
A handful of hand-picked prompts in a demo will almost always look impressive, because they were implicitly selected for the moments that work. Production traffic includes edge cases, ambiguous phrasing, adversarial input, and the long tail of real user behavior the demo never saw. Evals exist to close that gap by testing against representative, not cherry-picked, examples.
What an eval set actually is
At minimum, an eval set is a fixed collection of representative inputs paired with a way to judge whether the output was good — a reference answer, a rubric, or a programmatic check. The set should be stable across runs so that a score change reflects a real change in the system, not a different random sample of test cases.
Evals as a regression safety net
Every change to a prompt, model, retrieval pipeline, or tool can shift behavior in ways that are not obvious from a few manual checks. Running the same eval set before and after a change turns 'I think this is still fine' into a measured comparison, and catches regressions in edge cases a person would not think to re-test by hand.
Offline evals vs. online monitoring
Offline evals run against a fixed set before a change ships; online monitoring samples real production traffic and outcomes after it ships. Both matter: offline evals catch known failure modes early and cheaply, while online monitoring catches the failure modes your eval set didn't anticipate, since real users are more creative than any test-writer.
Building your first eval set
Start small: 20–40 real or realistic examples covering the ordinary case, a few known-hard cases, and a couple of clearly out-of-scope inputs the system should refuse or defer. A small, honest eval set you actually run on every change is worth more than a large one that's too expensive or slow to use regularly.
Practical exercise
Pick one AI feature you maintain or use. Write 15 test inputs covering typical use, a tricky edge case, and a case the system should decline or flag rather than answer confidently. Run the current system against all 15 and score each by hand — this is your first eval set, however small.
Sources and further reading
These primary or specialist references informed the concepts in this guide. Product details can change, so verify current documentation before implementation.