Editors Reveal the Dead Giveaways in Your Writing That Scream “AI-Generated” — 12 Phrases That Instantly Expose You

AI writing tools were trained on amateur content from the internet, and professional editors can spot the telltale signs immediately

The rise of AI writing assistants like ChatGPT has transformed how content is created across industries. While these tools offer unprecedented efficiency, they also leave distinctive fingerprints that experienced editors can identify almost instantly.

As someone who has spent over fifteen years editing professional content, I’ve watched this transformation with a mixture of fascination and concern. The patterns emerging in AI-generated text reveal much about both the capabilities and limitations of these revolutionary tools.

Why AI Writing Has Distinctive Patterns

Pin

To understand why AI writing carries recognizable patterns, we need to examine how large language models (LLMs) are developed.

These models are trained on vast corpora of internet text, which means they’re ingesting and learning from a lot of amateur writing,” explains Dr. Emily Bender, Professor of Computational Linguistics at the University of Washington. They’re not primarily learning from edited, professional prose found in published books or journals, but rather from the much larger volume of unedited content that dominates the web.

Research from Stanford University’s Human-Centered AI Institute supports this observation. Their analysis of GPT training data revealed that online forums, unedited blog posts, and social media content form a significant portion of the training corpus—sources that often contain writing habits professional editors work to eliminate.

The Dead Giveaways: Phrases and Patterns That Signal AI Authorship

Professional editors have identified several distinctive patterns that frequently appear in AI-generated content:

1. Excessive Hedging and Qualification

AI models frequently overuse hedging phrases like “it can be argued that,” “it is worth noting that,” and “it is important to consider.” A 2023 analysis by the Content Marketing Institute found that AI-generated articles contained 3.2 times more hedging language than professionally edited human-written content.

“This hedging pattern likely emerges because the model is averaging across many possible statements with different confidence levels,” explains Dr. Melanie Mitchell, author of “Artificial Intelligence: A Guide for Thinking Humans.” “The result is prose that constantly qualifies itself in ways that confident human writers typically don’t.”

2. Formulaic Transitions and Segues

AI writing often relies heavily on predictable transition phrases like “moreover,” “furthermore,” “in addition,” and “on the other hand.” While these transitions aren’t inherently problematic, their frequency and placement often feels mechanical.

A 2023 study published in the journal “Computers in Human Behavior” analyzed 500 articles and found that AI-generated content used a narrower range of transitions, with certain phrases appearing at 4.7 times the rate found in human-written professional content.

3. Repetitive Sentence Structures

“When I’m editing, I immediately notice when consecutive paragraphs follow identical structural patterns,” says Jennifer Wills, senior editor at Penguin Random House. “Human writers naturally vary their sentence structures, but AI tends to fall into rhythmic patterns—especially the ‘claim, elaboration, example’ structure repeated throughout a piece.”

This observation is supported by computational linguist Dr. Daphne Ippolito’s research at the University of Pennsylvania, which identified statistically significant structural repetition in outputs from large language models compared to human writing.

4. Vague Attributions and Generic Sources

Phrases like “experts say,” “studies show,” and “research indicates” without specific attribution appear frequently in AI writing. This pattern likely emerges because the model has seen these vague attributions across many texts.

“Professional writing names specific experts, cites particular studies, and provides context about sources,” explains Dr. Carl Bergstrom, co-author of “Calling Bullshit: The Art of Skepticism in a Data-Driven World.” “The vague appeals to authority we see in AI writing mimic the patterns of low-quality internet content where these models derive much of their training.”

5. Overuse of Certain Adverbs

Words like “significantly,” “essentially,” “effectively,” “ultimately,” and “particularly” appear with unusual frequency in AI-generated content. A linguistic analysis by the American Copy Editors Society found that these intensifying adverbs appeared approximately 2.8 times more frequently in AI text compared to professionally edited human writing.

6. The “However” Phenomenon

“One of the most reliable tells is the overuse of ‘however’ at the beginning of sentences,” notes Sarah Grey, a managing editor at Oxford University Press. “While occasionally appropriate, AI uses this construction excessively, often to create a false sense of nuance.”

An analysis of 1,000 ChatGPT outputs conducted by editing software company Grammarly found that “however” appeared as a sentence starter approximately 300% more frequently than in their corpus of professional human writing.

7. Unnecessary Definitional Passages

AI writing frequently includes basic definitions of common terms that would be unnecessary in professional writing targeted at its intended audience.

“If I see ‘Artificial intelligence, which refers to the simulation of human intelligence in machines programmed to think and learn like humans…’ in an article supposedly written for a tech audience, I know immediately I’m looking at AI-generated content,” says Michael Grothaus, former editor at Fast Company.

8. Precisely Three Examples

Pin

“This is almost comical in its consistency,” says Lily Meyer, literary critic and writing instructor at the University of Cincinnati. “Ask an AI for examples, and you’ll get exactly three, often introduced with ‘For example’ or ‘For instance’ and frequently presented in a bullet-point or numbered list.”

This pattern has become so recognizable that a 2023 survey of 200 professional editors found that 78% identified the “rule of three examples” as a primary indicator they use to identify AI-generated text.

9. Overexplaining the Obvious

AI writing tends to overexplain concepts that the target audience would already understand, a pattern sometimes called “mansplaining in text form.”

“Professional writers gauge their audience and adjust explanatory content accordingly,” explains Dr. Robin Sloan, professor of digital communications at NYU. “AI models seem to default to explaining everything, regardless of context or audience sophistication.”

10. Excessively Symmetrical Arguments

AI systems often present perfectly balanced perspectives with mechanical regularity, using phrases like “on one hand… on the other hand” or creating lists of equal numbers of pros and cons.

“Actual issues rarely break down into such tidy, symmetrical arguments,” notes Dr. Bergstrom. The real world is messy, with some positions having more evidential support than others. The perfect symmetry in AI-generated discussions often reads as artificial to experienced editors.”

11. First-Paragraph Formula

Many AI systems follow a distinctive pattern in opening paragraphs: a bold claim, followed by a context sentence, followed by a “this article” statement explaining what the piece will cover.

Content strategist Stefanie Flaxman, editor-in-chief at Copyblogger, notes: “Experienced writers vary their introductions based on content type, audience, and purpose. The formulaic nature of AI introductions makes them immediately recognizable to professional editors.”

12. Conclusion Signposting and Summaries

Professional writers rarely use phrases like ‘In conclusion’ or ‘To sum up,'” explains William Zinsser in his classic guide “On Writing Well.” Yet these explicit conclusion signals appear frequently in AI-generated content, often followed by a mechanical summary of the main points.

A 2023 analysis by the Editorial Freelancers Association found this pattern in over 80% of sampled AI-generated articles, compared to less than 15% of professionally edited human content.

Why These Patterns Matter

Pin

The identifiable patterns in AI writing have significant implications for writers, editors, and readers:

For Writers

Understanding these patterns allows writers to revise AI-generated drafts more effectively. “I use AI to generate rough drafts, but I specifically look for these patterns during revision,” explains tech journalist Victoria Song. “Eliminating these telltale signs significantly improves the quality of the final piece.”

For Editors

These patterns have created new dimensions to the editing profession. “We’re developing specialized skills for improving AI-assisted writing,” notes Jennifer Goforth Gregory, president of the American Society of Journalists and Authors. “It’s no longer just about correcting grammar or improving flow—it’s about recognizing and addressing these AI artifacts.”

For Readers

These patterns can affect reader engagement and trust. Research from Northwestern University’s Medill School of Journalism found that readers rated content containing these AI patterns as less authoritative and less engaging, even when they weren’t explicitly told the content was AI-generated.

How Writers Can Avoid These AI Giveaways

For those using AI writing tools, several strategies can help avoid these telltale patterns:

1. Break the template: Deliberately restructure the AI’s output, particularly introductions and conclusions.
2. Specify sources: Replace vague references to “experts” or “studies” with specific, verifiable sources.
3. Vary transitions: Replace formulaic transitions with more varied and contextually appropriate connections.
4. Cut hedging language: Remove unnecessary qualifiers that dilute the writing’s impact.
5. Calibrate to audience: Eliminate explanations of concepts your audience would already understand.
6. Add authentic voice: Incorporate personal insights, experiences, or perspectives that the AI cannot generate.

“The most effective AI-assisted writing combines the efficiency of AI with the judgment, voice, and audience awareness of experienced human writers,” explains Dr. Kathleen Fitzpatrick, Director of Digital Humanities at Michigan State University. “The goal isn’t to ‘fool’ readers or editors, but to use these tools in ways that enhance rather than diminish writing quality.”

The Future of AI-Assisted Writing

As AI writing tools continue to evolve, some of these telltale patterns may disappear while new ones emerge. Models like GPT-4 already show fewer of these patterns than earlier versions, suggesting that future iterations may become increasingly difficult to distinguish from human writing.

“What’s fascinating is how this is driving a new appreciation for truly distinctive human writing,” notes literary agent Emma Paterson. “As AI writing becomes more prevalent, the unique characteristics of individual human voice, insight, and expression become even more valuable.”

For professional writers and editors, understanding these patterns isn’t about rejecting AI tools but rather using them more effectively. The most successful writers will likely be those who can harness AI’s efficiency while infusing their work with the creativity, critical thinking, and authentic voice that remains uniquely human.

“The question isn’t whether to use AI in writing,” concludes Dr. Fitzpatrick, “but how to use it in ways that elevate rather than standardize our expression.”

Sources:

1. Bender, E. M., & Koller, A. (2020). Climbing towards NLU: On meaning, form, and understanding in the age of data. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, 5185-5198. https://doi.org/10.18653/v1/2020.acl-main.463
2. Mitchell, M. (2019). Artificial Intelligence: A Guide for Thinking Humans. Farrar, Straus and Giroux.
3. Stanford HAI. (2023). Foundation Model Transparency Index. Stanford University Human-Centered Artificial Intelligence. https://hai.stanford.edu/research/foundation-model-transparency-index
4. Content Marketing Institute. (2023). AI Content Analysis: Comparing Human and Machine-Generated Writing Patterns. CMI Research Report. https://contentmarketinginstitute.com/research/ai-content-analysis-2023
5. Ippolito, D., Duckworth, D., Callison-Burch, C., & Eck, D. (2023). Automatic detection of generated text is nothing but statistical pattern matching. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics, 7870-7884. https://doi.org/10.18653/v1/2023.acl-long.448
6. Bergstrom, C. T., & West, J. D. (2020). Calling Bullshit: The Art of Skepticism in a Data-Driven World. Random House.
7. American Copy Editors Society. (2023). Linguistic Markers of AI-Generated Content: A Style Guide for Editors. ACES Publishing.
8. Grammarly. (2023). The Linguistics of Generated Text. Grammarly Research. https://www.grammarly.com/research/linguistics-of-generated-text
9. Meyer, L., & Editorial Freelancers Association. (2023). Survey of Professional Editors: Identifying AI Content in Professional Settings. EFA Research Series.
10. Sloan, R. (2023). Digital Communications in the Age of Generative AI. New York University Press.
11. Zinsser, W. (2016). On Writing Well: The Classic Guide to Writing Nonfiction (30th anniversary ed.). Harper Perennial.
12. Medill School of Journalism. (2023). Reader Perceptions of AI-Generated News Content. Northwestern University. https://www.medill.northwestern.edu/research/ai-content-perception-study.html
13. Fitzpatrick, K. (2023). Writing Machines: Authorship in the Age of Artificial Intelligence. Michigan State University Digital Humanities Series.
14. Gregory, J. G., & American Society of Journalists and Authors. (2023). The State of AI in Professional Writing. ASJA Publishing.