What quality checks should precede onward licensing of a referral dataset?

Apply this checklist to the generated release before turning its contents into public catalogue claims.

- Trace expected labels to supplied evidence. - Validate record links and required field types. - Check task prompts for accidental answer leakage. - Keep related case families within one data split. - Inspect repetitive language across labelled outcomes. - Review grounding and potential disclosure risk. - Record unresolved defects and coverage exclusions.

Case-family separation matters when several records are variations of the same underlying situation. Placing one variation in training and another in evaluation can make performance look stronger without showing that the model handles unfamiliar circumstances. A reseller should ask how related records are identified and how split membership is assigned.

Language inspection serves a different purpose. If every positive referral begins with the same phrase, the dataset may reward a textual shortcut even when its labels are individually reasonable. Sample reviews should therefore include both record-level correctness and patterns across the release.

Generated output does not itself establish anonymity or privacy compliance. Grounding methods and unusual combinations still deserve inspection. Keep the review outcome specific: document which checks passed, what remains uncertain and which limitations must accompany downstream exports.