Ask a team how they know their AI system works and you’ll learn everything you need to know about how it will hold up. If the answer is “it feels good in testing,” it will not hold up.
Evals are the spec
For deterministic software, tests encode what “correct” means. For AI systems, evals play the same role — except correctness is fuzzier, so the evals matter more, not less. A labelled dataset that reflects real inputs, scored on the dimensions you actually care about, is the difference between engineering and hoping.
Calibration beats raw accuracy
The most useful thing an eval suite can tell you often isn’t “how accurate is it” but “does the system know when it’s unsure?” A model that’s wrong 5% of the time but flags those cases is deployable. One that’s wrong 2% of the time with total confidence is a liability.
A confidence score you haven’t validated against reality is just a decoration.
Build the harness first
Write the evals before the feature, run them in CI, and make the number visible. Then every change — a new prompt, a new model, a new chunking strategy — is a measurable bet instead of a leap of faith. Evals, or it didn’t happen.