AI features are probabilistic. That does not mean they must be mysterious. A strong evaluation practice turns vague expectations into testable behavior.
Define the job
Write down what success looks like for a real user. A support copilot might need to be correct, cite policy, protect private data, and respond within two seconds. A single accuracy number cannot capture all four.
Build a living test set
Collect real examples, edge cases, and deliberately adversarial inputs. Label the expected behavior, not just the expected wording. Run the set on every prompt, model, and retrieval change.
Measure the experience
Pair automated checks with human review. Track acceptance, correction time, escalation rate, latency, and cost. An answer that is technically correct but difficult to trust is not a successful feature.
Evaluation is how an AI product earns the right to become boring. Boring is a compliment when people depend on software.