How to Evaluate AI Systems with Real Code

A practical guide to evaluating GenAI systems, from a RAG assistant to a multi-agent researcher, grounded in real code you can open on GitHub, then the offline gate, online rollout, and monitoring lifecycle that keeps them honest in production.

Whatever you build on top of a language model, the same discipline keeps it honest. You never trust an output by default, you score it against examples whose answer you already know. This post walks it step by step with real code, build a golden set, evaluate a RAG app and then an AI agent, wire an automatic release gate, and keep evaluating after launch, plus the interview questions this method answers.