The uncomfortable part of shipping an AI feature is that the demo is not evidence. A language model will handle the example you chose beautifully and then fail on the fourth real document it sees. The gap between those two moments is where most AI projects quietly stall.
The fix is unglamorous: build the measurement before the feature. Every AI engagement we take on starts by assembling a labelled set of real inputs and expected outputs, and agreeing what accuracy has to look like for the feature to be worth launching.
Start with fifty real examples
Fifty is usually enough to be useful and small enough that a domain expert will actually sit down and label them. Pull them from production data, weighted toward the cases that matter: the awkward formats, the partially filled forms, the ones your operations team complains about.
Resist the urge to curate. A set made only of clean examples will report an accuracy figure that has nothing to do with the accuracy your users experience.
Score against a schema, not a vibe
For extraction and classification, define the output schema first and score field by field. This turns a subjective question into a set of comparisons a script can run in seconds, which means you can run it on every pull request.
For generative output, where there is no single correct answer, score the properties you care about separately — is it grounded in the source, does it stay inside the requested format, does it refuse when it should. Aggregate scores hide exactly the regressions you need to see.
Route uncertainty to a person
No threshold makes a model perfect, so design for the cases below it. A confidence score plus a review queue converts an unreliable automation into a reliable one with a smaller amount of human work attached.
The queue has a second benefit: every correction a reviewer makes is a new labelled example. The evaluation set grows from real usage rather than from another labelling session.
Put it in the pipeline
Once scoring is automated, wire it into continuous integration next to the test suite. A prompt change that drops extraction accuracy by six points should fail the build the same way a broken unit test does.
This is the whole point. Prompts are code with unusually high variance, and treating them as configuration that anyone can tweak without consequence is how quality degrades between releases without anyone noticing.
Working on something this touches on? Tell us about it — we are happy to talk it through whether or not it turns into a project.