Checklist evaluating sample AI outputs against quality criteria

Evaluating LLM Output Quality Without a Dedicated Eval Team

"Build an eval pipeline" is right advice that assumes resources most small teams don't have. Here's a version of that discipline that takes an afternoon, not a sprint.

📋 Build a Small Golden Set, Not a Big One

Ten to twenty real examples covering the actual range of inputs the system will see — including the tricky edge cases, not just the easy ones — is enough to catch regressions. A huge eval set is nice eventually, not a prerequisite to start.

👤 A Manual Review Cadence Beats No Review

Reviewing a handful of real production outputs weekly, scored against a simple rubric (correct, acceptable, wrong), catches drift and prompt regressions long before a fully automated eval pipeline would even exist for a small team.

🤖 When Automating the Eval Actually Pays Off

Once volume is high enough that manual review can't keep pace, or a prompt change needs to be validated against the golden set before every deploy, that's the point automating pays for itself — not before.