Defined term
Reviewer queue
A workflow where low-confidence or high-impact AI outputs route to a human for approval.
A reviewer queue is the production mechanism that combines AI throughput with human accountability. Low-confidence outputs (below a threshold), high-impact actions (above a stakes threshold), or random samples (for QA) are routed to human reviewers. The queue is instrumented with SLAs, reviewer agreement metrics, and feedback loops to improve prompts and models.
When it matters
Required for any AI workflow where mistakes have consequences. The mechanism that lets you ship at AI speed while keeping human accountability on the cases that matter.
Real example
A support reviewer queue handling 12% of cases flagged by confidence threshold + 3% random samples for QA. Reviewers process at 90 cases/day each, surface trends weekly, calibrate confidence thresholds monthly. Without it, the workflow would drift undetected.
KPIs to watch
Queue throughput (cases/reviewer/day), queue wait time P95 (<24h SLA typical), reviewer-AI agreement rate (signals calibration health, target: 85-95% range).
Related terms
AI governance
Policies, processes, and controls that make an AI system auditable and accountable.
Audit log
Tamper-evident record of every model input, output, version, and reviewer action.
Guardrails
Pre and post checks that filter unsafe, off-topic, or non-compliant model outputs.
Grounding
Anchoring model output to verifiable source material to reduce hallucination.
See it in action
We use this every week
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