How to Fact-Check AI Summaries by Treating Them as Claims, Not Prose

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AI-generated summaries can read smoothly while containing subtle factual distortions, such as conflating announcement dates with release dates or stripping conditions from statistics. An editorial workflow proposed in this piece advises reviewers to break AI drafts into discrete, checkable claims rather than proofreading them as flowing paragraphs. Each claim should be matched to a supporting passage from the original source, with attribution preserved and context verified, not just text matched. Editors are advised to keep factual statements, interpretive comments, and unresolved questions in separate sections of a draft to make the review process more concrete. The approach does not guarantee error-free output, but aims to create a visible decision trail documenting what was verified, what remains uncertain, and whether a draft was approved or held.

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