Resources / Guide · 10 min read
Evals That Actually Catch Regressions
Most AI eval suites are theater. Here is how to build ones that block bad releases and reward the right wins.
Start with golden examples
Twenty real inputs from your actual workflow with the answer you want. Not synthetic. Not "what a great answer looks like." Real ones. Run every change against them.
Three eval tiers
Tier 1: deterministic checks (does it produce valid JSON, does it cite a source). Tier 2: LLM-as-judge with a strict rubric. Tier 3: human review on a sample. Run all three; trust them in that order.
Regression discipline
Every prompt change runs the full suite. A drop on five out of twenty examples blocks the release. No "but it feels better" exceptions.
Production telemetry feeds the eval set
When you find a real failure in production, that input goes in the eval set. Within a quarter you have hundreds of real examples that catch real bugs.
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