Most people trying to spot AI writing look for the wrong thing: perfect grammar, or a vague sense that something reads "off." The actual tells are more specific and more consistent than that, and once you know what to look for, most AI-generated text gives itself away within a paragraph or two.
Inflated Claims About Ordinary Details
AI writing has a habit of treating routine facts as if they were significant turning points. A sentence establishing a founding date somehow becomes "marking a pivotal moment," or a color scheme "symbolizes the community's enduring connection to the land." Watch for phrases like stands as a testament, marks a shift, underscores its significance, and deeply rooted attached to details that don't actually carry that weight.
Shallow Analysis Bolted On With "-ing" Phrases
A related tell: a plain fact gets an "-ing" phrase tacked onto the end to make it sound like analysis. "The building uses local stone, reflecting the region's architectural heritage" is a filler clause, not an insight, it doesn't say anything the first half didn't already say. Real analysis names something specific; AI-generated analysis often just restates the fact in gerund form and calls it depth.
Forced Groups of Three
AI writing has a strong pull toward listing things in threes even when the underlying reality doesn't sort that neatly. "The event features keynote sessions, panel discussions, and networking opportunities" is the shape of the pattern more than a real description, three items chosen for rhythm, not because there happen to be exactly three relevant things.
Sentence Rhythm That's Too Even
This is the tell that matters most, and it's also what statistical detectors like GPTZero and Copyleaks are actually built to measure. Human writing varies: some sentences are short and blunt, others run long with several clauses, word choices occasionally surprise instead of always landing on the safest option. AI text, left to its defaults, produces smoother, more uniform sentences, one predictable length after another. Read a paragraph out loud. If every sentence has roughly the same rhythm and length, that's a stronger signal than any individual word choice.
Stock Vocabulary
Certain words show up in AI-generated text far more often than they do in typical human writing: delve, testament, underscore (as a verb), tapestry and landscape used as abstract nouns, pivotal, crucial, fostering, garner. None of these words are wrong on their own, but several of them clustered in the same paragraph is a strong signal.
Formulaic Structure
Beyond word choice, watch the shape of the piece itself:
- Bolded mini-headings inside a bullet list, where every item starts with a bold label and a colon, followed by a sentence that just restates the label.
- A stock "challenges and future outlook" section near the end that repeats vague optimism (the future looks bright, exciting times lie ahead) instead of adding a real fact.
- Repeated sentence openings, the same subject starting several sentences in a row, or a subject that keeps getting renamed (the protagonist, the main character, the central figure) instead of referred to consistently.
Why This Overlaps With What Detectors Catch
This isn't a coincidence. Tools like GPTZero, Turnitin, and Copyleaks score text on perplexity (how predictable each word choice is) and burstiness (how much rhythm varies from sentence to sentence). The patterns a careful human reader notices, uniform sentence length, formulaic structure, predictable phrasing, are the same statistical signal a detector is measuring, just read manually instead of scored automatically. See how detection actually works against a specific detector for the mechanics.
That overlap is why fixing these patterns for a human reader also improves how text scores against an actual detector, and why surface-level fixes (swapping a few words) rarely help either audience. A synonym swap doesn't change the sentence rhythm a reader unconsciously notices or the statistical pattern a detector is built to catch.
What to Do If You Spot These Patterns in Your Own Draft
- Read it out loud. Uneven, natural rhythm is hard to fake by eye alone, but the ear catches monotone sentence pacing quickly.
- Search for the stock vocabulary list. A handful of hits isn't damning, several in one paragraph is worth rewriting.
- Check bullet lists for the bold-label pattern. If every item follows label-colon-restatement, convert it to plain prose instead.
- Run it through a sentence-level AI Score Checker rather than relying on a read-through alone. A checker flags exactly which lines carry the statistical pattern, which is easy to miss even when you know what to look for.
FAQs
Can you reliably tell AI writing apart from human writing just by reading it?
Often, yes, especially with the patterns above stacked together. A single tell (one "delve," one forced group of three) proves nothing on its own. Several patterns clustered in the same piece is a much stronger signal than any one of them alone.
Is perfect grammar a sign of AI writing?
No, and this is a common false positive. Many human writers are edited or simply careful, and polish alone doesn't indicate AI involvement. The actual tells are about content patterns and sentence rhythm, not correctness.
Do AI detectors look for the same things a human reader would notice?
Largely yes. Detectors score perplexity and burstiness, essentially how predictable and how evenly-paced the text is, which is a formal version of the same unevenness a careful reader picks up on informally when something reads "too smooth."
Why does fixing these patterns also help with AI detector scores?
Because they're measuring related things. A detector's statistical model and a human reader's sense of "this feels templated" are both picking up on the same underlying pattern: predictable word choices and uniform sentence rhythm. Genuine structural rewriting, not word swaps, fixes both at once.
Is it possible to write AI-assisted content that doesn't show these patterns?
Yes. The patterns come from AI models defaulting to statistically safe, uniform choices, not from AI involvement itself. Content that's been restructured for genuine rhythm variation and specific, non-generic phrasing can be AI-assisted in its drafting stage and still avoid every pattern listed here.
Where can I check if my own writing has these patterns before I publish?
Run it through a sentence-level AI Score Checker, which flags specific lines rather than giving a single document-wide guess, so you can see exactly which sentences carry the pattern instead of re-reading the whole piece looking for it.
A detector flagged my writing as AI, but I wrote it myself. What's going on?
False positives happen, usually on writing that's unusually uniform for legitimate reasons: a technical style guide, a non-native writer following textbook grammar rules, or just a personal habit of even sentence length. The patterns in this article are the same ones a detector scores, so if your own writing genuinely has several of them, it's not the detector being wrong so much as your natural style overlapping with what the model was trained to catch. Varying rhythm deliberately tends to clear it.