An AI evaluation review set turns a much larger production stream into a bounded collection that domain experts can inspect. Sampling can target common tasks, failures, new features or uncertain cases rather than relying only on random selection.
The set supports annotation and judge calibration before examples are promoted into a golden dataset. Teams should record the sampling method because an unrepresentative review set can create misleading quality conclusions.
ELI5
An AI evaluation review set is a manageable group of model or agent cases chosen for people to inspect carefully. It turns a huge stream of outputs into a bounded sample for annotation and quality analysis.
For example, a team can select common support questions, known failures and uncertain answers for expert review. The selection method should be recorded because a set containing only easy examples can make the system look better than it is.
How should an AI evaluation review set be sampled?
It should combine representative traffic with important failures, edge cases and product segments relevant to the quality decision.
How is a review set different from a golden dataset?
A review set is selected for inspection, while a golden dataset contains reviewed examples with trusted annotations used for repeatable evaluation.
