What are AI evals- AI evaluation explained
responsibleai
AI evals are repeatable tests that measure whether an AI system actually does its job — here's how they work.
How do you know an AI system is actually doing its job? That's what an AI eval answers. Unlike traditional software, AI isn't deterministic — the same input can produce different outputs, models shift underneath you, and data drifts. So you don't test once and ship; you test continuously.
The three parts of every eval
Every eval is built from three pieces working together:
- Tasks — realistic examples of the actual work, including the hard, tricky cases that break weaker systems.
- What good looks like — a clear standard: a right answer, a scoring checklist, or a rule the output must follow.
- The grader — the piece that scores every output against that standard, run after run.
There are three common ways to grade, and most teams mix all of them:
- Code checks — fast and exact for format, correctness, or rule compliance.
- Human review — expert judgment where nuance matters and a rule can't capture quality.
- AI as judge — a model scores outputs against a rubric at scale.
What evals actually measure
A good evaluation strategy covers more than accuracy:
- Accuracy — is the output correct?
- Safety — does it stay within acceptable limits?
- Consistency — does it behave the same way across similar cases?
- Cost — how much does each answer take to produce?
- Speed — because a right answer that arrives too late still fails the job.
Run evals before launch, after every change, and continuously in production. If you can't measure it, you can't trust it — and evals are how AI earns that trust. Silverberry brings that discipline to your AI strategy and helps embed it through our Responsible AI-by-Design Framework.