Evaluating LLM Output

Evals, not vibes: how to measure whether an LLM feature actually works, catch prompt regressions, and ship changes with confidence.

1. Why Vibes Don't Scale You can't improve what you don't measure: eyeballing a few examples lies to you, and an eval is a real input set plus an expected behavior you can score.
2. How to Actually Grade Output The three ways to score model output - exact and rule-based checks, reference-based metrics, and LLM-as-judge - with where each one fits and where each one lies to you.
3. Evals as a Habit Run evals on every prompt change and model upgrade to catch regressions, track quality over time, and know where offline evals end and production monitoring begins.