How can a buyer inspect the quality of generated conditional decision data?
An explanation of structural, behavioural and realism checks a buyer or reseller can run on a generated sample before relying on it for training or evaluation.
Quality in this workflow can be inspected on three levels.
**Structure.** Every condition has a parent decision. Every evidence item references a condition. Timestamps are ordered: evidence cannot be reviewed before it is submitted, and a release cannot precede the decision. Enumerated states use a consistent vocabulary.
**Behaviour.** Records should follow the generated policies. If a policy says only a senior role can waive a condition, waivers in the data should come from that role, except where a deliberate exception has been included and labelled. Look at how often conditions end in each state, and whether the spread looks plausible for the domain.
**Realism.** Read a handful of cases end to end. Do handovers between roles make sense? Are rejected evidence and late submissions present? Does the wording read as business writing, or as repeated templates?
Also inspect for risk. Output is generated and informed by direct observation of how real businesses operate, but it is not real records. Look for text that seems to reproduce an identifiable person or organisation, check identifiers for plausible but non-real styles, and ask how grounding was handled.
A reseller can run structural checks automatically and sample the rest by hand before listing.
Quality can be inspected through structure, behaviour and realism checks, so resellers can pass on evidence of fitness rather than assurances.
- Audience:
- Licensed resellers
- Content type:
- Explanation
- Buyer question:
- How to evaluate it
Content release: r-a220eb6e099b243b
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