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AI review prompt templates for diagnosing drafts, checking evidence, organizing feedback, finding risks, and planning targeted revisions
Useful feedback names observable issues, their impact, and the evidence behind them. These recipes avoid guessing motives, separate blocking problems from preferences, and preserve the human reviewer’s judgment.
Find vague, exclusionary, stereotyped, or inconsistently applied language and replace it with job-relevant evidence criteria
Find language that permits incompatible implementations and expose the owner and evidence needed to decide
Trace factual and implied claims to evidence before copy is published
Compare a workbook with its intended schema before import, analysis, or automation
Identify wording risks in context and revise tone without weakening facts or boundaries
Diagnose observable writing problems without unreliable claims about whether AI produced the text
Find material differences in facts, scope, obligations, tone, and terminology across two language versions
Find unsupported claims, changed meaning, and material omissions before a summary is shared
Detect overlaps, missing transitions, impossible dependencies, hidden overtime, and capacity risk
Find instructions and output fields that reward unsupported completion, false certainty, invented sources, or concealed gaps
Find narrative, slide, visual, and speaking-load problems without deleting decision-critical evidence
Review scale, encoding, labels, missing context, and the claim against source values
