Foundations

Why evals matter, the maturity model, and what makes LLM evaluation hard.

The five pages here lay out the case for evals, the maturity ladder you climb, and the reasons LLM evaluation does not look like classical ML eval. Read them in order if you are new. Skim if you are already shipping.

Most teams arrive thinking the gap is tooling. The gap is usually conceptual: a half-built mental model of what evals are for, and which rung of the ladder pays off this quarter. The pages below fix that before you touch a framework.

Chapters:

Why evals matter. The case for evals as the moat, the three reasons LLM features rot, and what a useful eval actually looks like.
The eval maturity model. Five rungs from vibe-checking to a continuous quality program, with the smallest useful artifact at each rung.
AI development as scientific method. The observe-hypothesize-experiment-measure loop mapped onto an AI product team.
Why LLM evals are hard. Non-determinism, subjectivity, prompt sensitivity, and why classical ML eval frameworks fail.
Eval vs monitoring vs guardrails. Three concepts that share traces but do different jobs, and where each fires in a request lifecycle.

AI Evals

The most useful, opinionated reference for AI evals.

AI Evals is a curated technical reference for evaluating large language model systems in production. The site covers error analysis, LLM-as-judge calibration, RAG evaluation, agentic eval, statistical rigor, and twenty task-specific eval playbooks. Every claim is cited.