Establish checkpoints where humans review AI-generated work for quality, appropriateness, and alignment with system goals. Even with validation, human judgement remains essential for nuanced decisions.
Human Review Gates
How to
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Map the workflow
Identify where AI generates work in your process: initial exploration, spec drafting, code generation, documentation writing. Insert review points at each stage.
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Define review criteria
Create role-specific checklists: designers check brand alignment and UX, developers check technical feasibility, accessibility specialists check inclusive design.
- Check for: Correct token usage (no hardcoded values), all interaction states included, accessibility attributes present, prop naming follows conventions, edge cases handled, component fits within existing patterns.
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Assign review responsibility
Clarify who reviews what. Use RACI to avoid ambiguity. Ensure reviewers have authority to reject or request changes.
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Set review depth
Not everything needs deep review. Light review for low-risk outputs (test cases, initial drafts), thorough review for high-impact work (component APIs, specs).
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Provide review training
Teach reviewers what to look for: common AI mistakes, system-specific requirements, quality standards. Share examples of good and problematic outputs.
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Track review outcomes
Monitor approval rates, common rejection reasons, and time spent reviewing. Use this data to improve Rules for AI Generation and reduce review burden over time.