"Evals: the missing test suite for AI features"
Most teams would never merge code without tests, yet ship prompt changes with no safety net at all. A practical guide to building an eval suite: golden sets from real traffic, field-level precision and recall, and a CI gate that fails the build on regression.
Somewhere on your team, someone changed a prompt last week. Maybe they added a clarifying sentence, tightened the output format, or swapped the model version to save money. How do you know it didn't make something worse? If the honest answer is "we ran it a few times and it looked fine", you are shipping blind — the exact thing your team would never tolerate from ordinary code.
Have an AI feature stuck between demo and production?
The gap — reliability, evals, cost control, the plumbing that keeps it running unattended — is exactly the work I do. If that sounds familiar, a short conversation is usually enough to point you the right way.
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