01
Define failure before you define success
List the ways the feature can hurt users: wrong facts, unsafe actions, tone misses, latency spikes. Those become your first eval cases.
A small, ruthless golden set beats a large, vague benchmark nobody maintains.
02
Automate the boring checks
Run regression evals on every prompt or model change. Catch format breaks, empty answers, and known failure prompts in CI.
Human review still matters for nuance — but machines should catch the obvious regressions first.
03
Ship behind a quality gate
Agree on a minimum bar: accuracy on the golden set, p95 latency, and escalation rate to humans.
If a change fails the gate, it does not ship — no matter how impressive the demo looks in a slide deck.
