Human review remains essential for many AI systems: safety nuance, tone, domain correctness and the "does this help" question. The challenge is operational: review can become slow, inconsistent and expensive if it is not designed.
Design review as an operating loop
Human review should feed decisions: prompt changes, retrieval improvements, policy tuning and tool hardening. If review produces scores that do not translate into action, it becomes a cost center.
Use queues, not ad-hoc sampling
Queue design makes review scalable:
- High-risk queue. Safety-critical intents and regulated workflows.
- Drift queue. Random samples that detect slow regressions.
- Incident queue. Cases linked to escalations and user complaints (see incident response).
- Experiment queue. Cases used to compare variants (see experimentation).
Calibrate reviewers and control variance
Most review programs fail because reviewers disagree. Use calibration:
- Shared examples and scoring discussions.
- Gold standard items to measure reviewer consistency.
- Clear rubrics and definitions (see evaluation rubrics).
Sample intelligently
Do not sample uniformly. Bias review toward where risk and impact are highest:
- New releases and prompt changes (see prompt regression testing).
- Workflows with high usage or high value (see usage analytics).
- Workflows with high error rates (see error taxonomy).
Close the loop with engineering and governance
Review findings should create actionable tickets: prompt changes, retrieval fixes, better citations, or tool contract improvements. For high-risk outcomes, route findings into governance artefacts and audits (see compliance audits).
Human review works when it is treated like a product: clear inputs, clear outputs, and a feedback loop that improves the system over time.