How do I test whether my AI feature actually works before launch (evals, in plain terms)?

An eval is just a repeatable test suite for your AI feature: 30-100 real example inputs, the output you expect, and a way to score each run. Start by reading your model's actual outputs and labelling errors by hand (error analysis); patterns you find become automated checks, either simple code assertions or an LLM grading another LLM. Teams that skip this iterate blind; teams that build even a scrappy eval loop ship better AI faster than competitors.

A quick orientation. The real value is below: resources worth your time, from people who've actually done it.