Designing a Useful AI Evaluation Review Queue

# Designing a Useful AI Evaluation Review Queue

A review queue is not a storage area for unresolved records. It is a controlled path from uncertainty to a documented decision.

Define queue states

Use states that explain what happens next: discovered, awaiting source check, duplicate review, needs editorial review, approved, rejected, and stale. Each state should have an owner and a transition rule.

Put evidence beside the candidate

A reviewer should see the canonical URL, source result, content hash, discovery connector, duplicate candidates, category suggestion, and reason for the current state. Avoid making the reviewer reconstruct the history from unrelated logs.

Protect publication

New records should begin non-public. Approval should require the required source checks and metadata, not simply the existence of a row. A rejected record should remain excluded from search and sitemap output.

Prioritize intelligently

Sort by freshness, user impact, source confidence, and age. A stale public record and a newly discovered duplicate may need different queues. Do not prioritize only by arrival time.

Preserve decisions

Record who changed the state, when, and why. When a reviewer rejects a candidate, the reason should help the connector improve or prevent the same candidate from returning indefinitely.

A good review queue gives humans the exceptions while keeping ordinary verification and cleanup automated.