The gap between "it worked in the demo" and "it works in production" is where most AI projects quietly die. The bridge across that gap is evaluation — a repeatable way to measure whether the system is actually getting better or just differently broken.
Write the eval before the feature
A good eval set is a collection of real inputs and the outputs you would accept. Build it first, from actual user data, and it becomes both your spec and your regression test. Change a prompt, run the evals, see the score — no more guessing whether that tweak helped.
Measure what the user feels
Accuracy is not the only axis. Latency, tone, refusal rate and cost all shape the experience. We track them together, because an answer that is correct but takes twelve seconds is still a bad answer.
Evals turn AI from a slot machine into engineering. They are unglamorous, and they are the single highest-leverage thing a team can build.