As the Content marketing team at Yarnit, we live a fascinating dual existence.
Every day, we help build and refine an AI platform that generates marketing content for thousands of brands. And every day, we edit AI-generated content to ensure it truly connects with human readers.
This unique position has given us an intimate view of both AI's incredible capabilities and its persistent quirks. The technology continues to evolve rapidly, producing increasingly human-like content, but we're also realists. Even as AI advances, certain patterns in machine-generated writing still benefit from a human editor's touch.
These insights come from a place of enthusiasm about what AI content platforms are accomplishing. They've transformed our team's productivity, allowing us to focus on strategy and creativity instead of grinding through first drafts.
We believe in transparency about where AI currently stands. It's revolutionizing marketing but still evolving. The most successful content teams understand this balance – they leverage AI's speed and capabilities while applying human expertise to elevate the final output.
In this guide, we'll walk you through the 10 most common patterns in AI-generated content. For each pattern, we'll share practical editing techniques you can apply right now to make any AI content read like it came from a human who genuinely cares about your audience.
1. Those Academic Transitions Nobody Actually Uses
"Moreover." "Furthermore." "Consequently." "In addition to."
These formal transitions are the most obvious tells in AI writing. They instantly make content feel stiff and academic rather than conversational and engaging.
This pattern persists across all AI platforms and content types. We still encounter it regularly when reviewing AI-generated drafts.
Last week, we edited a product description that read: "Our coffee maker brews in under three minutes. Moreover, it features a programmable timer. Furthermore, its compact design saves counter space."
We rewrote it as: "Our coffee maker brews in under three minutes. It also includes a programmable timer, and its compact design saves valuable counter space."
The difference feels subtle but significantly impacts how readers connect with the content.
Prompt Tip: When using any AI platform, add this specific instruction: "Write conversationally with natural transitions. Avoid academic phrases like 'moreover' and 'furthermore'." Being explicit helps the AI adjust its style.
Editing Solution: We've trained our team to hunt for these formal transitions: moreover, furthermore, consequently, nevertheless, thus, additionally. We replace them with conversational alternatives: also, plus, so, but, and. Sometimes, we simply start a new sentence. This single change immediately elevates the content's authenticity.
2. Those Generic Intros That Put Readers to Sleep
"In today's fast-paced world..."
If your AI-generated content opens with this phrase (or its equally clichéd cousins "In an era of digital transformation" or "In recent years"), delete it immediately. Same goes for conclusions beginning with "In conclusion" or "To summarize."
These generic bookends plague AI writing across all platforms. Completely eliminating these defaults remains challenging for all AI systems.
A fitness client's draft recently started with: "In today's busy world, finding time for exercise can be challenging." This could have introduced literally any fitness article published in the last decade.
We rewrote it to: "The average American spends 7 hours daily looking at screens but just 17 minutes on intentional physical activity. Our new fitness tracker aims to flip those numbers."
The revised version provides immediate value: specific data, clear contrast, and a concrete promise.
Prompt Tip: We tell our clients to explicitly direct the AI with: "Start with a surprising statistic about [topic]" or "Begin with a specific customer scenario showing [problem]." The more specific your opening instruction, the less likely you'll get a generic intro.
Editing Solution: Our editing process mandates completely rewriting generic intros and conclusions. These positions deserve special attention since they create first and last impressions. Ask yourself: "Would this make ME want to keep reading?" If not, start fresh with a specific hook—a surprising stat, provocative question, or concrete example.
3. All That Needless Hedging That Weakens Impact
AI models (including ours) are programmed to avoid making definitive claims, resulting in content filled with hedging language like "may potentially," "could possibly," "might be considered," and "appears to indicate."
This happens because AI systems are trained to be cautious—they don't want to state something definitively that might be incorrect. But this caution often goes too far.
I recently edited an article containing this sentence: "This approach may potentially offer what could be considered significant benefits under certain conditions." Strip away the hedging, and you get: "This approach offers significant benefits."
Direct. Clear. Confident.
At Yarnit, we're tuning our models to be more assertive in appropriate contexts, but this pattern persists. Until AI can perfectly gauge when certainty is warranted, this will require human judgment.
Prompt Tip: Instruct the AI to "write with confidence and avoid unnecessary qualifying language like 'may,' 'might,' and 'could'" when generating content on topics where certainty is appropriate.
Editing Solution: I train our editors to search for these hedge words: may, might, could, potentially, possibly, seems, appears, perhaps. For each instance, we ask if the qualification genuinely serves accuracy or legal protection. In most cases—roughly 80%—these qualifiers can be safely removed, instantly strengthening the content's impact.
4. That Empty Corporate Jargon Nobody Finds Meaningful
"Our innovative solution leverages cutting-edge technology to revolutionize the customer experience paradigm."
What does this actually mean? Nothing.
AI systems (Yarnit included) have been trained on thousands of press releases, white papers, and corporate websites filled with meaningless business-speak. They've learned to mimic this language, often stringing together impressive-sounding words that communicate very little.
I recently had to revise an AI draft describing a simple email software update as "a revolutionary paradigm shift that leverages cutting-edge technology to optimize the synergistic relationship between users and their communication ecosystem."
After translation into human language: "A helpful update that makes sending emails faster and more reliable."
We're actively expanding our AI's training to emphasize clear, concrete language over jargon, but this remains a work in progress across all platforms.
Prompt Tip: Explicitly instruct the AI to "use plain language that clearly communicates value. Avoid business jargon like 'leverage,' 'synergy,' and 'innovative solution.' Explain benefits in concrete terms." Being specific helps our models adjust their outputs.
Editing Solution: My team keeps a running "jargon detector" list: leverage, synergy, optimize, paradigm shift, cutting-edge, revolutionary, streamline, innovative solution. When we spot them, we ask: "What specific benefit or feature is being described?" Then we rewrite using concrete language that clearly communicates actual value. Your readers will thank you.
5. Those Worn-Out Metaphors That Need Retirement
AI writing frequently relies on clichéd metaphorical language: "paints a vivid picture," "unlocks the potential," "takes your business to the next level," "shines a light on opportunities."
These expressions are so overused they've lost all impact. They're the literary equivalent of elevator music—boring background noise that readers simply tune out.
At Yarnit, we're working to train our models on more diverse figurative language, but clichés persist because they're so common in training data. Even as we improve, this will likely remain an area where human creativity adds significant value.
I recently revised an AI-generated piece about data analysis that packed three tired metaphors into a single paragraph: "paints a vivid tapestry of consumer behavior," "unlocks the hidden potential of your data," and "illuminates the path forward."
Despite the seemingly descriptive language, none of these expressions actually explained what the product did.
Prompt Tip: Ask the AI to "use concrete examples and specific details rather than common metaphors. If using analogies, create original ones relevant to [your industry]." This pushes our models to be more specific and less reliant on stock expressions.
Editing Solution: Highlight all metaphorical language in your draft. Replace most with concrete, specific descriptions of actual functions or benefits. When figurative language would enhance the content, craft fresh analogies relevant to your specific context. Instead of "unlocks the potential of your data," try something specific: "transforms confusing spreadsheets into five actionable insights your sales team can implement today."
6. That Perfect Balance That Humans Never Actually Use
One of the most subtle yet pervasive patterns in AI writing is its unnatural symmetry and balance:
"The program enhances productivity, improves efficiency, and boosts morale."
"Whether you're a beginner seeking basics, an intermediate learner refining skills, or an expert pursuing mastery, this course offers value."
Human writers rarely maintain such perfect structural parallelism. Natural writing has variation in rhythm, sentence length, and pattern. It breathes. It occasionally breaks rules for emphasis. It uses fragments.
This pattern is particularly challenging because it's not about specific words or phrases—it's a structural issue embedded in how AI generates text. At Yarnit, we're experimenting with techniques to introduce more natural variation, but this remains one of the hardest patterns to solve algorithmically.
Prompt Tip: This pattern is difficult to address through prompting alone—it's fundamentally how current AI models construct text. We've had limited success with instructions like "vary sentence structure and length considerably," but this isn't a complete solution.
Editing Solution: This requires human intervention. Identify perfectly balanced sentences, especially those with exactly three parallel elements (a common AI pattern). Break the symmetry by:
- Converting some parallel elements into standalone sentences
- Varying sentence lengths dramatically
- Adding occasional sentence fragments for emphasis
- Reading content aloud to catch unnatural rhythms
When I edit content with too-perfect balance, I intentionally introduce a bit of human messiness. Trust me—it reads better.
7. Those Unnecessarily Complex Words Nobody Uses
Why say "use" when you could "utilize"? Why "start" when you could "commence"? Why "do" when you could "implement"?
Because real people don't talk like thesaurus entries.
AI systems (including ours) often default to more complex vocabulary—they've been trained on formal writing that tends to use bigger words. At Yarnit, we're actively training our models to prefer simpler language, but this pattern persists.
I recently edited a how-to guide that instructed users to "utilize the implementation functionality to commence the process" when it could have simply said "click start to begin."
Clear writing typically uses simple words. The shorter, more common term almost always works better.
Prompt Tip: Tell the AI to "use simple, everyday language" or even "write at an 8th-grade reading level." This significantly reduces the vocabulary complexity in the output.
Editing Solution: Hunt down needlessly complex words: utilize/use, commence/start/begin, sufficient/enough, purchase/buy, obtain/get, require/need, terminate/end, facilitate/help, etc. Unless the more complex word adds precision or fits a specific formal context, swap in the simpler option. Your content will instantly become more readable and relatable.
8. That Exhaustive Thoroughness That Tests Patience
AI doesn't know when to stop. It creates overly comprehensive lists and over-explains obvious concepts, testing readers' patience.
This happens because AI models are designed to be thorough and cover all bases. At Yarnit, we're working on better gauging appropriate depth for different content types, but getting this balance right remains challenging for all AI platforms.
I recently received an AI article that spent three paragraphs defining what a budget is ("a financial plan that allocates expected income toward expenses, savings, and debt repayment") before finally getting to the actual budgeting tips promised in the headline.
Nobody needs this level of explanation for familiar concepts.
Prompt Tip: Be specific about scope in your prompts. Instead of asking for "a comprehensive guide," try "a focused guide covering the 3-4 most important aspects of [topic]." Also, specify "assume the audience already understands basic concepts in this field."
Editing Solution: Be ruthless with the delete key. Cut explanations of concepts your audience already knows. For lists, ask whether each item truly adds value or if you're just padding the word count. At Yarnit, our editors typically remove 20-30% of AI-generated content during revision—and what remains is always stronger.
9. Those Generic Examples That Feel Manufactured
"John, a 35-year-old marketing executive, found that the new software increased his productivity."
"Consider Sarah, a small business owner who implemented these strategies and saw immediate results."
These stock characters and vague scenarios lack any authenticity or memorability. They feel manufactured because they are—AI models create generic examples because they can't draw on real experiences.
At Yarnit, we're enhancing our knowledge base to include more specific, real-world examples, but this remains an area where human input adds significant value.
I've seen case studies so generic they could apply to literally any company: "Company X implemented the solution and saw significant improvements in efficiency."
Readers crave specificity.
Prompt Tip: When asking for examples, provide specific parameters: "Include an example featuring a restaurant in Chicago that solved this specific problem" or "Use examples from healthcare with specific metrics." The more specific your prompt, the better our AI can tailor its examples.
Editing Solution: Replace generic examples with either real case studies (ideal) or fictional ones with highly specific details. Instead of "a business owner saved time," write "a one-location hardware store in rural Minnesota saved 15 hours weekly on inventory management." Specific details—even in fictional examples—create credibility and memorability.
10. That Arm's-Length Approach to Opinions
AI struggles with expressing authentic opinions, resorting to constructions like "this product is generally considered to be effective" or "many people find that this approach offers benefits."
This distanced, third-person framing occurs because AI models are trained to be objective and avoid making definitive subjective claims. At Yarnit, we're working to enable more natural opinion expression when appropriate, but this remains challenging across all AI platforms.
I edited an AI-generated restaurant recommendation that stated: "The flavor profile is commonly described as rich and complex with balanced elements of sweetness and acidity."
Nobody talks like that after enjoying a great meal.
Prompt Tip: When opinions are appropriate, instruct the AI to "write in first person from the perspective of someone who has direct experience with [the topic]" and to "include specific personal reactions."
Editing Solution: Rewrite subjective sections to include clear opinions where appropriate. Replace "it is considered" or "many find" with direct statements. For that restaurant example: "The dish delivers a perfect balance of rich flavors, with just enough sweetness to complement its bright acidity."
The Human-AI Partnership: Making Content Shine
AI technology continues to evolve, with each generation producing more naturally human content. But we're also realists—we know the human touch still matters.
The most successful content teams don't see AI as a replacement for human creativity and judgment. They see it as a powerful partner that handles the heavy lifting of first drafts, allowing humans to focus on refinement and strategic thinking.
What once took our team days now takes hours, without sacrificing quality. The marketers who thrive aren't those who simply hit "generate" and publish. The advantage comes from knowing how to effectively prompt, edit, and refine—preserving AI efficiency while adding the human elements that forge genuine connections.
Next time you review AI-generated content, keep this guide handy. With practice, you'll quickly spot these patterns and transform mechanical first drafts into content that sounds like it came from someone who truly understands your audience.
Because that's the ultimate goal, isn't it? Not just content that ranks well or fills space, but content that genuinely connects with the humans reading it. That's what all of us working at the intersection of AI and marketing are building toward—and what we hope this guide helps you achieve today.
