How Wikipedia Spots AI-Written Prose - and What Writers Can Learn
We all get that itch: "Was this written by a model?" The tricky part is proving it. One community has put in the reps - Wikipedia editors - and their playbook is sharp.
Since 2023, editors have been running Project AI Cleanup. With millions of edits flowing in, they've distilled clear patterns of AI prose. The big lesson: you won't catch it with a detector. You'll catch it by reading like an editor.
The headline insight: automated tools won't save you
Detection software is noisy and easy to fool. Instead, the editors look for stylistic habits that show up online at large but are rare in tight, well-sourced writing. That's your filter.
Common tells Wikipedia flags
- Inflated importance. Drafts over-explain why a topic "matters," leaning on generic phrases like "a pivotal moment" or "a broader movement" instead of specific impact, data, or citations.
- Hype-by-association. Long lists of minor media mentions to imply notability. It reads like a personal bio, not independent coverage.
- Present-participle tails. Sentences trail off with hazy claims: "emphasizing the significance…" or "reflecting the continued relevance…" It feels like a conclusion without evidence.
- Vague marketing language. Everything is "scenic," "breathtaking," "clean and modern." As the editors put it, it starts to sound like a TV commercial transcript.
- Over-smooth tone. No friction, no specificity, and repeated synonyms that pad the paragraph without adding content.
- Mushy sourcing. Reliance on press releases, personal sites, or derivative blogs instead of independent, high-quality references.
Practical fixes writers can use right now
- Cut hollow importance. Replace grand claims with concrete facts, dates, and results. If it matters, you can show it.
- Rewrite those tails. Turn "emphasizing the significance of…" into a clear clause with a subject, verb, and proof. Example: "The policy cut emissions by 18% in a year."
- Swap adjectives for measurables. "Breathtaking views" → "2,800-meter vantage with a 200-km sightline."
- Trim puffery. Media mentions belong if they're substantial and independent; otherwise, cut them.
- Reduce filler words. Words like "currently," "notably," "importantly," and "additionally" often mask weak sentences.
- Read it out loud. If you'd never say it, edit it. If you can remove a sentence and lose nothing, remove it.
Editor triage: five quick questions
- Does the draft establish significance with independent, reliable sources?
- Are examples proportionate, or are they random shoutouts to pad the piece?
- Can quotes and claims be traced to primary reporting or high-quality analyses?
- Does the tone slip into promotion?
- Are names, numbers, and dates precise and verifiable?
Why these patterns persist
Models learn from broad internet text, which over-represents marketing copy and padded prose. They're great at fluent sentences, weaker at earned specificity. You can disguise the tells, but they leak through under time pressure or weak prompts.
That's why the Wikipedia guidance hits: it targets habits baked into how models generate text, not one-off keywords that change with each release.
Go deeper
Start with Wikipedia's community guidance on large language models and content quality. It's direct, evidence-based, and worth bookmarking. Read the overview.
If you're a working writer who uses AI but wants to keep your voice sharp and your sourcing solid, skim practical resources and tool roundups here: AI tools for copywriting.
The takeaway
Don't rely on detectors. Read for habits. Cut inflation. Add evidence. Make every sentence pay rent. If more writers and editors apply that filter, fluff loses its cover - wherever it comes from.
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