Error analysis beats generic metrics: a practical framework for AI evals
PawelHuryn · x · 2026-11-12
Paweł Huryn shares an AI evals methodology (with a full infographic): generic metrics like hallucination or toxicity miss domain-specific issues — top teams analyze traces and let app-specific failure modes emerge bottom-up.
- Core loop: add traces → open-code problems → axial-code failure modes → re-code traces, until saturation; 100 diverse traces is the rule of thumb.
- Synthetic traces are optional but require failure hypotheses and human review.
- Build automated evals only after error analysis, one metric per evaluator: code-based (deterministic, cheap) vs LLM-as-Judge (narrow, binary checks first).
- Key alignment metrics: TPR and TNR.
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