Evaluation turns desired behavior into tests, examples or metrics that can be applied consistently. It may measure correctness, reliability, safety, cost, latency or the quality of a complete workflow.
Useful evaluations should resemble the conditions that matter in practice and include meaningful failures. A score is evidence about the tested cases, not proof that the system will succeed in every future situation.
ELI5
Evaluation is the process of checking whether an AI system does the job people expect. The tests should measure the qualities that matter in real use, such as correctness, safety, usefulness, speed, and cost.
For example, a medical summarizer should be tested on whether it preserves important facts and avoids unsupported claims, not only on whether its writing sounds fluent. Different applications therefore need different evaluation criteria.
What can an AI evaluation measure?
It can measure correctness, task completion, safety, reliability, speed, cost and other defined qualities.
Why should evaluations include failures?
Failure cases reveal limits and reduce the chance that a system looks strong only because the test set contains easy or unrepresentative examples.
