Evaluations before demos
Anyone can build an impressive language model demo in an afternoon. The evaluation suite is what tells you whether it still works next month.
The gap between a demo and a product is not model quality. It is that a demo is measured by whether it impressed the room, and a product is measured by whether it behaves correctly on the thousandth request, in front of someone who is annoyed.
The mechanism that closes the gap is unglamorous: a golden dataset drawn from real traffic, including the edge cases and the failures, with expected outcomes attached. Everything else — prompt changes, model upgrades, routing — becomes a measurable decision against it.
Without one, teams end up in a loop we see constantly. Someone tweaks a prompt to fix a complaint, nobody checks what else changed, and three weeks later a behaviour that used to work has quietly regressed. Nobody can say when, because there was never a baseline.
Scoring can be partly automated with a model as judge, but only after it has been calibrated against human ratings on the same examples. An uncalibrated judge is a confident random number generator, and it will happily tell you quality improved.
Once the suite exists, it goes into CI as a gate. A prompt change that drops the score does not merge. That single rule turns AI work from something that feels risky into something that behaves like the rest of your engineering.
It is also the only honest answer to a client asking whether the thing works. You can show them a number, produced from their own data, before anyone has spent a budget.