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A prompt can ask for a particular voice, but it cannot reliably enforce every house preference across drafts. A style lint rule can make those preferences visible during editing by flagging recurring issues for a writer or editor to review. It should help improve the writing—not claim to identify who wrote it.
Why does AI writing sound generic?
Generic writing often relies on familiar transitions, broad claims, predictable structures, and polished but low-specificity phrasing. Those patterns can appear in AI-generated text, but they are not exclusive to it: people use them too. A checklist of stylistic habits can guide revision, but no individual phrase or construction proves that a passage came from AI. A community-maintained field guide makes that caveat explicit and recommends aiming for clearer writing rather than evading detectors: AI Smells: Agents Please Avoid These.
A prompt is still useful for giving a model a direction. The practical limitation is consistency: an instruction such as “sound more like us” does not necessarily spell out all the choices your editors expect, or make those choices easy to check across many drafts. A lint rule turns selected editorial preferences into review prompts. That is a practical workflow argument, not a result established by a direct study comparing prompts with linting.
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What should a writing lint rule do?
A lint rule checks a draft against a publication’s chosen standards and points to something a person can evaluate. It should not assign an “AI” score, infer authorship, or treat every flagged pattern as an error. Its purpose is to make a concrete editorial issue easier to see.
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Build checks from your own editorial standards
Start with examples of published work and specific guidance from your editors. Translate only objective, useful preferences into checks. Depending on your publication, a rule might flag a repeated stock transition, an unsupported factual claim that needs a source check, or a required review step that has not been recorded. These are examples of possible editorial checks—not signs that a writer or model produced the text.
Make each warning actionable and overridable
A useful warning points to the relevant passage, explains the editorial reason, and gives the writer a choice: revise it, accept it, or dismiss the warning. Editors need to be able to override a rule when a phrase or construction is right for its context. Avoid blanket bans on ordinary words or punctuation; they can generate false positives and push writers toward one flattened house voice.
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Keep the check focused on the work
Evaluate a rule by what it measures, whether its warning suggests a useful action, how often it misfires or narrows voice, whether an editor can override it, and whether it preserves source review and disclosure practices. These are practical evaluation criteria, not a tested comparison of linting products.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchCan a style checklist detect AI authorship?
No. A style pattern is not proof of authorship, and a lint rule should not be used as an AI detector. Even tools designed for detection have limitations. Turnitin says its AI writing model may misidentify human-written, AI-generated, and AI-paraphrased text and should not be the sole basis for adverse action. Its report is also limited to qualifying long-form prose and has format and language constraints. Consult Turnitin’s current AI Writing Report guidance for the applicable scope and requirements.
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Human judgment is not infallible either. In a 2025 study of 300 non-fiction English articles, the majority vote of five people who frequently used ChatGPT for writing misclassified one article. The study authors report that participants relied on lexical clues as well as broader qualities such as formality, originality, and clarity. That result applies to this sample and task; it does not show that people can reliably identify AI writing in general. See Russell, Karpinska, and Iyyer, “People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text” (ACL 2025, pp. 5342–5373).
Use editorial review to judge whether the work is clear, accurate, supported, and suitable for its audience—not to turn stylistic clues into an authorship verdict.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to use AI assistance responsibly
Writers remain accountable for AI-assisted work. CDC guidance for scientific work advises authors to review and validate AI output, avoid citation inaccuracies, and disclose substantive AI use in line with relevant organizational and publisher rules. The CDC summarizes possible disclosure elements as “Content Affected + Action Taken + AI Tool + Purpose of AI Use + Human Oversight.” Its guidance is specific to scientific work, not a universal legal rule for every writer; requirements vary and can change. Check the policy that applies to your publication. Read the CDC’s Considerations for Disclosing Generative AI Use in Scientific Work.
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For citation conventions, The Chicago Manual of Style’s FAQ says generated text should be credited, provides ChatGPT note examples, and advises noting when generated text has been edited. Its answer is based on the 18th edition (2024); follow the current policy and citation style required by your publisher. See The Chicago Manual of Style Online FAQ.
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A short checklist for putting a rule in place
- Choose the standard. Identify a recurring editorial preference from your publication’s own guidance and examples.
- Write a narrow check. Make it flag a reviewable issue, not a supposed marker of AI authorship.
- Explain the warning. Show the passage and tell the writer what to examine or verify.
- Allow human judgment. Let writers and editors revise, accept, dismiss, or override the flag.
- Review the rule itself. Remove checks that create noise, suppress appropriate voice, or encourage edits that weaken clarity.
- Keep accountability intact. Verify claims and citations, review AI-assisted output, and follow the applicable disclosure policy.
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