AI output varies, so a demo that looks good proves little. Evals turn quality into something you can measure: a set of real cases, such as past invoices or customer questions, each with an agreed correct outcome. Every change to a prompt, model or data source is run against the set before release.
For example, a contract review tool might be tested on past contracts with known clauses to confirm it still finds the termination terms after a model upgrade. The misconception is that evals are a one-time launch check. Business cases change, so the test set should grow whenever the system gets something wrong in production.