An AI evaluation set can expire without a model change

A test set built during the first pilot may omit new document types, languages or customer categories. Repeating it produces continuity without establishing current relevance.

Record what the set represents

Describe the collection period, intended population and material exclusions. Keep this information linked to the dataset version.

Datasheets for Datasets recommends documenting composition and recommended use. Those details help a team decide when an evaluation set needs review.

An illustrative assistant initially serving one product line may later support several. Its earlier test remains useful historical evidence but does not automatically cover the expansion.

Add new evidence without destroying comparability

Maintain suitable stable tests while introducing representative new cases. Report which result comes from which population.

Review labels and source authority when the business process changes. A formerly correct answer may no longer be valid under the current product or policy.

Avoid filling the set only with failures discovered after launch. Challenge cases are useful, but they answer a different question from a representative performance sample.

Assign review triggers

Product launches, corpus changes and material shifts in usage can justify reassessment. Do not rely only on an annual calendar if the application changes faster.

Mark each evaluation report with the population and system versions it covers. If the application expands, the earlier result can remain available without being presented as universal assurance. This is a practical way to preserve comparability while keeping users and buyers informed about the boundary of the evidence.

GAT’s enterprise AI deployment guide emphasises practical evaluation. Keeping the test population current preserves the meaning of that evidence and prevents a familiar score from becoming an outdated reassurance.