Data types
AI review and feedback
Existing review records can show how employees assessed, corrected, accepted, or rejected an AI-generated output during business work.
A complete task has context.
Illustrative exampleThe request
An AI tool drafts a reply to a customer’s delivery question.
The work
An employee removes an unsupported date, adds a confirmed option, and records the reason for the change.
The result
The corrected reply is approved. The original and final versions remain linked.
A buyer might study error patterns or use existing feedback for a permitted learning task. Review the feedback for consistent labels, task context, and permitted use before preparing it for a buyer.
What to inventory
Saved output revisions, approval histories, documented review comments, and recorded quality decisions.
- The original output and the approved or rejected version
- Review criteria, reasons, and reviewer roles where recorded
- Task context, model or tool version where known, and the final decision
What to review
- Does permission cover the original inputs, output, and employee feedback?
- Are review labels consistent enough to compare?
- Can the record distinguish a factual correction from a style preference?
Your records are a good place to start.
Tell us what your team records. We will help you understand the next questions to ask.