Evals, Explained in Plain Language

Evals are standardized tests used to measure AI system performance, requiring unseen questions, scored answers, multiple test types, and comparisons across model versions to track trends over time. The piece explains that a single score can hide regressions, such as a model improving in reasoning while slipping in coding, and emphasizes monitoring scores across releases on relevant tests.

Evals, Explained in Plain Language You can't just feel that an AI system got better. You need a test — and a way to grade it. Give it a test it's never seen If it's already seen the answers, acing the test proves nothing. A real eval is questions it has never met before . Score every answer Every answer gets checked against the right one. No vibes — just a tally at the end. One test isn't enough Good at math doesn't mean good at everything. Run lots of different tests — one score alone can hide a lot. Compare the old model to the new one "Better" isn't automatic. The new model can climb in reasoning and safety and still slip backwards in coding, all at once. Watch the score over time One release can dip — that's normal. What matters is the trend across many releases, on the tests that actually matter to you. Evals — that's the real name for it.