Prompt evals and regression tests: the missing discipline in AI product teams

AI features change more often than traditional features because providers, models, and prompts evolve quickly. Without evaluation, teams end up relying on vibes and a few manual spot checks.

A simple eval set can be small: 50 to 200 examples. What matters is coverage: include easy cases, edge cases, and the failure patterns you see in production.

Define a scoring rubric that maps to user value. “Helpful and correct” beats “sounds smart.” For structured outputs, validate schemas and compare to expected labels.

Run evals when you change prompts, models, retrieval settings, or parsing logic. Automate it in CI for high-impact features, or at minimum as part of a release checklist.

Track metrics over time. When quality drops, you want to know whether it correlates with a provider change, a prompt edit, or new content entering the system.

Use human review strategically. Pair quantitative scores with a small weekly review where product and support look at failures and label them. This is where roadmap decisions become obvious.

Evals turn AI development into engineering instead of gambling. It’s the difference between “it worked yesterday” and “we can ship this with confidence.”