Evaluation articles

Evaluation is how you know whether a change to a prompt, model or retrieval setting made an LLM application better or worse. Without it, teams ship on impressions from a handful of manual tests and discover regressions from users.

Articles here cover building small test sets you trust, choosing metrics that reflect what users care about, measuring retrieval separately from generation, using LLM-as-judge without inheriting its biases, and running evaluations in CI so quality is checked on every change rather than once before launch.

For a structured path through this topic, see LLM Evaluation.