Evals: The Most Underrated Skill in AI Engineering, From Error Analysis to Versioned Golden Datasets
sudoraohacker · x · 2026-10-29
A developer argues evals are the most underrated skill in AI engineering and lays out an actionable methodology:
- Start with error analysis, not metrics: sample 100+ production traces, annotate failures via open coding, cluster them into categories, and let the top 3 categories define your first metrics
- Build a versioned golden dataset: store inputs, expected outputs/rubrics and metadata (intent, difficulty, user segment), stratify so rare-but-critical cases survive, and version it alongside prompts for reproducibility
- Generate synthetic data along explicit dimensions: define persona × intent × complexity × edge-case tuples and convert each into a natural input for coverage instead of near-duplicates
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