An AI review queue organizes outputs that need a person's decision before use. The queue should show why an item requires review and what will happen after approval, correction or rejection.
Prioritize by consequence and urgency rather than arrival time alone. Give each reviewer the input, proposed result and relevant evidence. Define categories for missing information, factual errors, policy questions and technical failures so unresolved work reaches the appropriate team.
Track ownership and prevent duplicate approvals. Keep the reviewed version connected to the action it authorizes; a later edited draft should not inherit approval silently. Provide escalation and expiry rules for items that cannot be resolved promptly.
Review backlog, correction rates and sampled approved outputs. A short queue does not prove good quality if difficult cases are being accepted without adequate scrutiny. Use observed failures to improve the model workflow and review instructions.
Open Sources Used
This page uses open and institutional references as a frame; the final decision still belongs to the company record, threshold and owner.
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