Why You Must Read AI Traces Before Writing Evals

Standard advice suggests picking automated benchmarks and metrics first, but practitioners argue that effective AI evaluation requires reading raw interaction traces by hand. Discover how grounded theory methods like open and axial coding expose criteria drift and application-specific flaws before tools are deployed. The discussion covers practical trace sample sizes, how to spot upstream root causes, and why generic out-of-the-box eval suites often mislead engineering teams. Key sources: • Hamel's Blog (Hamel Husain & Shreya Shankar), published 2025-06-27, updated 2026-09-01; retrieved 2026-11-08. — https://hamel.dev/blog/posts/evals-faq/why-is-error-analysis-so-important-in-llm-evals-and-how-is-it-performed.html • Hamel's Blog, published 2025-07-24; retrieved 2026-11-08. — https://hamel.dev/blog/posts/evals-faq/what-parts-of-evals-can-be-automated-with-llms.html • Anthropic, Demystifying evals for AI agents; retrieved 2026-11-08. — https://www.anthropic.com/research/demystifying-evals-for-ai-agents Episode 6498 — myweirdprompts.com/n/6498 (permanent short link: myweirdprompts.com/864651) Listen on Android: MWP FM Player — myweirdprompts.com/apk