"Building your first golden set: 30 examples beat zero"
Most eval projects stall at the empty dataset, not the scoring harness. Thirty well-chosen examples from real traffic beat zero by an enormous margin. How to harvest them, label them, and make sure the ugly cases are in.
Most eval projects don't die at the scoring harness. They die at the empty dataset. The team agrees the AI feature needs tests, someone builds a runner in an afternoon, and then the real question lands: where do the test cases come from? Nobody owns the answer, a ticket called "build a proper benchmark" goes on the backlog, and six months later prompt changes are still shipping unchecked.
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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