# Evals, Explained in Plain Language > Source: > Published: 2026-08-22 12:00:00+00:00 # 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.