How to Evaluate an LLM Feature Before It Ships

"I tried it on a few examples and it worked" is the most common thing we hear when a team describes how they've tested an AI feature, and it's not an evaluation, it's a demo. The gap between "worked on the examples I happened to try" and "reliable enough to put in front of customers" is exactly where AI features go wrong after launch, and it's closeable before launch if you build a real evaluation process instead of an informal one.

A real evaluation set starts with actual queries, not invented ones. Pull real examples from support tickets, search logs, or however users would actually interact with the feature, not the clean, well-phrased questions a developer writes while building it. Real users ask ambiguous questions, make typos, and phrase things in ways your test cases never anticipated if you wrote them yourself.

Each example needs a labeled correct answer, decided by someone who actually knows the domain, before you run anything against the model. Without a labeled answer to compare against, "how did it do" becomes a judgment call made in the moment, which is exactly the kind of inconsistent evaluation that lets a feature ship on vibes.

Size the set to the actual variance in your inputs, not to a round number that feels thorough. Fifty well-chosen examples that cover your real query patterns, including the hard ones, catch more regressions than five hundred examples that are all slight variations of the easy case. We build these sets iteratively, starting small and adding examples as new failure patterns show up in production.

The set only earns its keep if it's re-run on every change, not just before the first launch. A prompt tweak, a model upgrade, a change to what gets retrieved before generation, each of these can improve some cases and quietly break others. Re-running the same labeled set after every change is what catches "we fixed the thing we noticed and broke three things we didn't."

Track failures by category, not just as a pass rate. A single percentage tells you something's wrong; it doesn't tell you what to fix. Separating failures into buckets, wrong retrieval, correct retrieval but wrong reasoning, correct answer but wrong tone or format, tells you whether the next fix belongs in your data, your prompt, or your retrieval pipeline.

Decide what "good enough to ship" means before you see the results, not after. It's easy to rationalize a mediocre score once you're staring at it and want to launch. Setting a threshold in advance, and what happens to failures below it (human fallback, a lower-confidence UI treatment, blocking the feature entirely), keeps the evaluation honest instead of becoming a formality you talk yourself past.

None of this is a one-time gate before launch. The evaluation set is a living asset: every real production failure that gets reported becomes a new labeled example added to it, so the set gets better at catching the exact failures your users actually hit, not just the ones you anticipated when you built it. A feature evaluated once before launch and never again is a feature you're flying blind on within a month of shipping it.