AI Evals Explained: From Fundamentals to Practical LLM Evaluation

About This Guide

This gist takes someone from zero prior exposure to AI evaluation through to a working, defensible evaluation practice. It’s been revised against a detailed technical review: claims that were previously stated as absolute rules have been corrected into calibrated, defensible statements, and several missing chapters have been added - evaluation dimensions, dataset design, agent evaluation, metamorphic and adversarial testing, regression and CI/CD, and production monitoring.

The guide is organized into five layers, moving from mental model, to evaluation design, to failure investigation, to advanced evaluators, to system-specific evaluation - followed by appendices for interview prep, hands-on labs, a glossary, and a checklist.

Full guide: gist.github.com/beinghumantester