Section 036 · Chapter 6, Building Evals That Matter
Evals and Benchmarks
Benchmarks are useful signals, but many evals are narrower, noisier, or less well-defined than their leaderboard numbers suggest.
benchmarkevals benchmarks
What to do
- Ask what the eval actually measures, how labels were created, how failures are judged, whether the task still reflects reality, and whether the metric matches the product decision.
- Use public benchmarks for broad signals.
- Use domain evals for product-specific quality.
- Use adversarial suites for known risks.
- Use live sampling for current reality.
Evidence to preserve
- Track task validity, label quality, contamination risk, environment drift, oracle ambiguity, metric fit, and inter-rater agreement.
- Preserve the inputs, versions, configurations, raw outcomes, and results for benchmark, evals benchmarks needed to reproduce work on Evals and Benchmarks.
- Report results for benchmark, evals benchmarks by relevant slice, separate blocker failures from averages, state uncertainty and blind spots, and connect the result to a release decision.
Expert note
When the system matters, audit benchmarks before trusting them. Track task validity, label quality, contamination risk, environment drift, oracle ambiguity, metric fit, and inter-rater agreement. A leaderboard score is an input, not a release decision.
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Cite this page
Jason Arbon. "Evals and Benchmarks." Testing AI Knowledge Edition, section 36.
https://jarbon.ai/testing-ai/knowledge/ch036-evals-benchmarks.html