Behind the Craft AI Evaluations Crash Course in 50 Minutes (2025) | Hamel Husain
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Sep 28, 2025 
Hamel Husain, an expert in AI evaluation methods, shares his vast experience training PMs and engineers at top tech firms. He breaks down how to effectively analyze real production traces, emphasizing the power of binary pass/fail ratings over complex scoring systems. Hamel explains common pitfalls in evaluation metrics and introduces practical tools for continuous monitoring. Listeners gain insights into building simple annotation tools and the importance of grounding evaluations in real problems to drive meaningful improvements.
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Analyze 100 Traces First
- Look at raw traces and write simple notes about failures before anything else.
- Do this ~100 times to surface common, actionable problems quickly.
Use Spreadsheets + LLMs To Classify Issues
- Export your manual notes to a spreadsheet and use an LLM to propose categories.
- Use pivot tables to count categories and prioritize fixes based on frequency.
Prefer Code Tests For Deterministic Bugs
- Some problems are best solved with code-based tests, not LLM judges.
- Use reference-based checks for deterministic issues (e.g., date handling).