Evaluation Is the Product, Not an Afterthought

Most AI features are built backwards: ship the prompt, watch the demo, then wonder in production why quality drifts. Evaluation gets bolted on after the first incident. By then you are debugging blind.

Evaluation is not a final gate; it is the instrument you steer with. Without it, every prompt tweak is a guess and every model swap is a coin flip, because you cannot see whether you improved anything or just moved the failures somewhere new.

A useful eval is concrete. It is a fixed set of real cases with known-good outcomes, scored the same way every time, run on every change. It includes the ugly inputs, such as the empty field, the adversarial phrasing, and the edge that broke you last month, so regressions are caught before users find them. It reports a number you trust enough to block a release on.

Build the eval first, or at least early, and development becomes a feedback loop instead of a guessing game. You change one thing, you see the score move, you keep or revert. The teams that ship reliable AI are not the ones with the best prompts. They are the ones who can measure whether a prompt is better.