Master LLM evals step by step: read 100 traces by hand, then build a versioned golden dataset
datawithsuman · x · 2026-10-30
A detailed learning path for LLM evals arguing that error analysis must precede metrics:
- Build something to break first: a small app with a real failure surface (RAG over docs, text-to-SQL, tool-using agent). Traces matter more than polish.
- Read 100 traces by hand: dump inputs, outputs, retrieved chunks and tool calls into a spreadsheet, annotate failures in plain English, cluster and count them. Your top 3 categories become your first metrics.
- Build a versioned golden dataset: 50 cases from top failure categories plus 20 happy-path and 10 adversarial cases, with rubrics and metadata, committed to git and tagged against every prompt change for reproducibility.
- Generate synthetic data along explicit dimensions rather than asking an LLM for generic test questions.
The core thesis: evals are the most underrated skill in AI engineering, and open-coded failure analysis beats premature metric design.
Related event: Evals: The Underrated Core Skill for AI Engineers(2 posts)→
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