What are AI evals?

AI evals are structured tests that measure whether an AI system does what you need it to do. Each eval checks one behaviour on realistic examples and returns a score or a review, so a business can tell whether a change made the system better or worse, instead of judging it by impressions.

Why it matters for your business

AI output varies: OpenAI's guidance notes that models sometimes produce different output from the same input, so ordinary software testing is not enough. Without evals, you find out about failures from customers. With them, decisions such as switching model, changing instructions or extending the system to a new team rest on a repeatable measure instead of a hunch.

How it works

The usual sequence is to review real outputs to find how the system actually fails, turn the failures that matter into specific tests, and run them whenever something changes. Checks can be simple code rules, review by people, or another AI model acting as a judge, depending on what is being measured. The test set grows over time as new failures appear.

Example

Picture a 60-person software company with a support agent that cancels orders. Its evals check two things on a set of 100 past tickets (illustrative): did the agent pick the right order, and did it wait for the cancellation to succeed before telling the customer it was done. Before changing the underlying model, the team reruns the set and compares the scores.

Costs, limits and when not to use it

Someone has to build and maintain the test set, and expert human labels are costly and slow to produce. An AI judge has known biases: OpenAI's guidance lists a preference for longer answers and sensitivity to the order of responses, so check its agreement with human reviewers before relying on it. Public benchmark scores say little about your own product. For one-off drafting where a person reads every output, formal evals are usually overkill.

AI evals vs benchmarks

Benchmarks compare general-purpose models on shared tasks and can help you pick a starting model. Product evals test your own system, with your data, prompts and tools, against the failures that matter to your business. Evals complement guardrails, which block unwanted behaviour as it happens, and human-in-the-loop review.