What should we inspect before using generated review material?
Quality inspection should cover operational plausibility, rule consistency and disclosure risk, while recognising that generated output does not itself establish privacy compliance.
Inspect the generated output and its supporting documentation before admitting a review pack into a training or evaluation collection.
- Ask how business observations informed workflow structure, distinguishing those design inputs from the generated records being supplied. - Check that roles, handovers and exceptions form a coherent process that a domain reviewer can explain without inventing missing steps. - Verify that generated policies agree with task expectations and do not require mutually incompatible decisions from the same evidence. - Inspect for unexpected identifiers, distinctive wording or unusual combinations that warrant further review of possible source resemblance. - Confirm that any claims about realism describe the represented process and do not imply delivery of underlying business records. - Record unresolved grounding and disclosure questions, with an explicit decision about whether each prevents the proposed use.
Synthetic output does not automatically establish anonymity, privacy compliance or regulatory approval. A buyer can ask MirrorFirm for information sufficient to inspect grounding and disclosure risk within the proposed scope. These checks address the suitability of the material itself; they should remain visible even when the agent performs well on the tasks.
Treat business grounding and disclosure inspection as separate acceptance checks for generated evaluation material.
- Audience:
- Evaluation teams
- Content type:
- Checklist
- Buyer question:
- How to evaluate it
Content release: r-a220eb6e099b243b
Related questions
- What quality checks should precede training on generated review cases?
- How can a late event reveal whether an agent checks its assumptions before committing a decision?
- Which evaluation hooks make review runs reproducible and auditable?
- How should evaluation distinguish a sound review decision from a persuasive explanation?
- How can we tell whether an agent responds to meaningful evidence rather than superficial cues?
- How should buyers inspect realism and disclosure risk in generated review material?