Testing real tasks
Suppose an AI assistant answers customer support questions. A team might give it common and tricky questions, then check whether its replies follow policy, use the right facts, and avoid harmful mistakes. These questions and standards for judging the replies form a test set.
Some tests have one right answer; others need people or software to judge the response. Teams also measure response time and cost. They record results as a baseline, then run the same tests after changing the model, instructions, or tools.
Why one score is not enough
A good average score can hide serious failures on rare tasks or for particular groups. Public tests may not reflect a product’s users, and software that judges answers can also be wrong. Teams combine repeatable tests with human review and keep checking results after launch.