Words like delve, unlock, elevate, tapestry, and testament to show up so often in AI writing that readers now recognize them on sight. None of them are banned by LinkedIn. They just quietly signal generic, unedited AI output, which reads as less personal and gets less engagement than specific, voice-driven writing.
The AI words to avoid on LinkedIn fall into two buckets: overused single words (delve, leverage, elevate, foster, underscore, tapestry, testament, robust, game-changer, multifaceted, seamless, comprehensive, utilize) and stock phrases (in today's fast-paced world, unlock the power of, navigate the landscape of, it's important to note that, let's dive in, at the end of the day, it's not just X it's Y). Cut them, replace vague claims with a specific number or moment, and end with a specific question instead of a generic "thoughts?".
TL;DR: swap generic AI vocabulary for plain, specific language, fix the structural tells (vague openers, hedge clauses, low-effort closers) at the same time, and the post stops reading like a template.
None of these words are wrong in isolation. The problem is frequency: when three or four of them show up in one short post, it reads as unedited AI output rather than something a specific person wrote.
| AI phrase | Why it flags as AI | Write this instead |
|---|---|---|
| Delve into | One of the single biggest AI tells. A 2024 academic study tracking word frequency in scientific writing found usage of 'delve' spiked sharply the same year ChatGPT launched. | Look at / dig into, or just state the finding directly. |
| In today's fast-paced world | A generic scene-setter AI defaults to when it has no specific detail to open with. Readers have seen this exact opener hundreds of times. | Open with the actual number, result, or moment instead. |
| Unlock the power of | A marketing-flavored intensifier that describes nothing concrete about what the thing actually does. | Say what it does. 'Cuts your prep time in half' beats 'unlocks the power of automation.' |
| Elevate | A vague upgrade verb used across nearly every AI-generated business post regardless of context. | Improve, raise, or name the specific outcome. |
| Navigate the landscape of | Combines two AI favorites (navigate plus landscape) into a phrase generic enough to describe literally any topic. | Name the actual challenge. 'Handle X' or 'deal with Y.' |
| Foster | AI's default verb for 'encourage' or 'build', reads corporate and impersonal in a first-person post. | Build, encourage, grow. |
| Underscore | AI's stock word for 'show' or 'highlight' whenever it summarizes a point. | Shows, proves, confirms. |
| Embark on a journey | Turns any ordinary task into a travel metaphor. AI reaches for this to open almost any narrative. | Just say what you started doing. |
| It's important to note that | A hedge clause AI inserts before nearly every claim. It adds zero information and delays the actual point. | Delete it. State the claim directly. |
| Testament to | A formal linking phrase ('a testament to our commitment') that AI uses to connect one example to a bigger claim. | Shows, proves. |
| Tapestry | A flowery metaphor AI reaches for whenever describing anything varied or complex. | Describe the actual mix directly. 'A mix of X, Y, and Z.' |
| Multifaceted | A vague adjective substituting for the actual facets, without naming any of them. | List the actual parts. |
| Robust | An overused intensifier for 'strong', 'reliable', or 'thorough' that adds no real meaning. | Use the specific quality: reliable, well-tested, durable. |
| Game-changer | A hyperbolic claim AI defaults to for anything even mildly positive, regardless of actual scale. | Describe the actual change and how big it is. |
| Seamless | Claims a frictionless experience without describing what specifically requires no extra steps. | Describe the actual step that got removed. |
| Comprehensive | A vague scale word, common padding before 'guide', 'solution', or 'overview.' | State what is actually included. |
| Utilize | A longer, more formal synonym for 'use' that AI reaches for by default to sound more authoritative. | Use. |
| Furthermore, Moreover, Additionally | Stacking two or three formal transition words back to back across consecutive sentences is a hallmark of AI paragraph structure. | Start the sentence plainly, or cut the transition entirely. |
| Let's dive in | A filler transition before the actual content that a human writer would usually just skip. | Delete it. Start with the content. |
| At the end of the day | A vague closing phrase AI uses to wrap up a post without adding a real conclusion. | State the actual takeaway in one plain sentence. |
| It's not just X, it's Y | AI's favorite rhetorical contrast structure, deployed whether or not the contrast is actually true or interesting. | Only use this structure when the contrast is specific and real. |
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A breakdown of the words AI writing tools default to most often, why they read as a giveaway, and what to swap them for in real writing.
Illustrative examples showing the same idea written first in generic AI-speak, then rewritten with a specific number, moment, or opinion attached.
In today's fast-paced world, we are thrilled to unlock the power of our new dashboard, a true game-changer that will elevate your workflow.
We shipped a new dashboard today. It cuts report time from 20 minutes to 90 seconds. Here's exactly what changed and why we built it this way.
As I embark on this new journey, I want to underscore the multifaceted lessons that have fostered my growth and served as a testament to hard work.
I got laid off in March. Six months later I signed my first client. Here's what actually happened in between.
When you navigate the landscape of project management tools, it's important to note that each platform boasts a comprehensive, robust feature set.
I tried 4 project management tools this quarter. Two were worth keeping. Here's what separated them.
At the end of the day, building a strong team isn't just about hiring, it's about fostering a culture of trust. Thoughts?
I've hired 11 people in 3 years. The best ones all failed a different interview question than I expected. Which question would you add?
Read the whole draft out loud once
The fastest filter for AI-sounding copy is your own ear. If a sentence is something you would never say to a colleague across a table, it needs a rewrite. Reading aloud catches stiff transitions and hollow adjectives far faster than re-reading silently.
Search for the banned-word list and remove every hit
Search your draft for delve, leverage, unlock, elevate, foster, underscore, tapestry, testament, robust, and game-changer. Every match gets replaced with a plain, specific word or cut entirely.
Replace the generic opener with a specific detail
If the first line is 'In today's fast-paced world' or any scene-setter that could apply to any topic, throw it out. Start instead with the actual number, moment, or result the post is about.
Cut every hedge clause
Phrases like 'it's important to note that' or 'it should be mentioned that' add zero information. Delete the clause and keep the claim that follows it.
Break up stacked formal transitions
If two or three sentences in a row start with Furthermore, Moreover, or Additionally, cut at least two of them. Real writers rarely stack more than one formal transition in a short LinkedIn post.
Add one concrete number
AI drafts tend to stay abstract. Adding a real number (a dollar figure, a percentage, a time span) anchors the post in something specific and immediately reads less like generic filler.
Rewrite the closing line into a specific question
Replace 'Thoughts?' or 'Agree or disagree?' with a question that is answerable in one or two sentences from personal experience. Specific questions get more, and better, comments.
Keep one or two imperfect turns of phrase
Contractions, sentence fragments, and a slightly unusual word choice are what make writing sound like a specific person rather than a smoothed-out average. Do not sand every edge off the draft.
Have someone else read it and ask one question
Send the draft to a colleague and ask only: does this sound like me? Their gut reaction is a better AI-tell detector than any automated checker.
Feed the model your own voice next time, not a blank prompt
The root cause of AI-sounding output is usually a generic prompt with no reference for your voice. Tools built around your actual writing samples, rather than a one-line prompt, produce far fewer tell words to begin with.
Prompting once and pasting the output straight to LinkedIn. A single generic prompt gives the model nothing to anchor its tone to, so it defaults to the same overused phrasing it uses for everyone else asking a similar question.
Swapping words but not fixing structure. Replacing 'delve' with 'explore' does not fix a five-paragraph post that still opens with a scene-setter, hedges every claim, and closes with 'thoughts?'.
Over-correcting into forced slang. Swinging the other way into exaggerated casual tone, random emoji-style enthusiasm, or slang that does not match your normal voice reads just as inauthentic as AI-speak.
Never giving the tool your own past posts as reference. Most AI writing tools default to generic corporate tone unless they are shown real examples of how you actually write. Skipping this step guarantees more tell words, not fewer.
Trusting an AI-detector score as proof either way. Public AI detectors have high false positive and false negative rates on short-form social copy. A low or high score is not reliable evidence. Reading the post aloud is more accurate than any detector badge.
Editing only the first paragraph and leaving the rest untouched. AI tell words cluster less in hooks (which creators tend to rewrite by hand) and more in the middle and closing paragraphs, which get pasted with little editing.
No. A blank one-line prompt reliably produces generic phrasing because the model has no reference for how you actually write. A tool that is pointed at your own past posts or your product's specifics produces drafts that sound noticeably more like you, because it is not defaulting to the statistical average of all business writing.
No, there is no platform rule against any specific word. Delve is simply overrepresented in AI output relative to how people write about their own work casually, so it functions as a stylistic signal to readers, not a rule enforced by LinkedIn itself.
Public AI detectors are unreliable on short-form social copy, with meaningful false positive and false negative rates. Reading the post aloud and checking it against the banned-word list above catches more real problems than trusting a detector's score.
"Delving into ChatGPT usage in academic writing through excess vocabulary" tracks word-frequency shifts across millions of scientific abstracts and finds usage of words like delve rising sharply after ChatGPT's release, evidence the study's authors use to estimate the scale of AI-assisted writing.
Kobak et al., arXiv 2024 preprintWikipedia's editing guideline "Signs of AI writing" catalogs the exact vocabulary and structural patterns (stock phrases, over-formal transitions, empty editorializing) that editors use to flag likely AI-generated text, most of which map directly onto the LinkedIn tell words above.
Wikipedia: Signs of AI writingWikipedia's overview of "AI slop" documents how the term became shorthand for low-effort, generic AI-generated content flooding social platforms, and why readers have grown quick to recognize its tell-tale vocabulary and structure.
Wikipedia: AI slopDeleting banned words helps, but it treats the symptom. The actual cause of AI-sounding posts is usually a blank, generic prompt with no reference for how a specific person writes. Tools like Lifast work from your own product details and past writing instead of a one-line prompt, which means far fewer of these tell words show up in the first draft to begin with.
Large language models are trained to produce the statistically likely next word across an enormous body of formal writing, and that training data leans heavily toward academic, corporate, and journalistic prose rather than the casual, specific way most people actually talk. Words like delve, underscore, and foster show up constantly in that formal training mix, so the model reaches for them by default even in a casual first-person LinkedIn post.
A widely cited 2024 study on excess vocabulary in academic writing found that usage of words like delve rose sharply the same year ChatGPT became widely available, a pattern that only makes sense if a meaningful share of that writing was AI-assisted. The same dynamic plays out on LinkedIn: the words are not banned or flagged by any platform rule, they are simply statistically overrepresented in AI output relative to how people naturally write about their own work.
That is the core distinction worth remembering: there is no LinkedIn algorithm rule against the word delve. The problem is that clusters of these words together read as generic and impersonal to a human reader, and readers scroll past generic content faster than specific, voice-driven content, which quietly caps reach even without any explicit penalty.
Deleting every word on the banned list is necessary but not sufficient. A post can be entirely free of delve, leverage, and tapestry and still read as generic if it opens with a scene-setter, hedges every claim with 'it's important to note that', and closes with a low-effort 'thoughts?'. Those structural habits are just as much a tell as the vocabulary itself.
The fastest single improvement is usually structural: replace the vague opener with a specific number or moment, and replace the vague closer with a specific, answerable question. Those two changes alone move a post from reading generic to reading personal, independent of any single word swap.
This is also why simple find-and-replace word lists only get creators partway there. The words are a symptom. The underlying cause is a lack of specificity: no real number, no real moment, no real name attached to the claim. Fixing the cause fixes the symptom automatically.
Human writing on LinkedIn tends to share a few traits that AI output rarely produces without deliberate direction: a specific number or dollar figure early in the post, a sentence fragment or two, an opinion stated plainly without hedging, and a closing line that invites a specific, personal answer rather than a generic one.
None of this requires abandoning AI tools entirely. The distinction is between a blank generic prompt, which reliably produces the tell words above, and a tool that is given real examples of how a specific person writes and generates drafts tuned to that voice rather than to the statistical average of all business writing on the internet.
For creators who want to keep the speed of AI drafting without the generic tell words, the fix is directional, not prohibitive: point the tool at your own voice and your own numbers instead of a one-line prompt, then apply the ten-step checklist above before publishing.
Straight answers to the questions people ask right after realizing their post reads like a template.
The most frequently flagged words and phrases include delve, unlock the power of, elevate, foster, underscore, tapestry, testament to, robust, game-changer, multifaceted, and opening lines like 'in today's fast-paced world' or 'in the ever-evolving landscape of.' Closing a post with only 'thoughts?' is also a common tell. The full lookup table above lists 20-plus of the most common ones along with what to write instead.
Language models predict statistically likely words based on their training data, which leans heavily toward formal academic and corporate writing where words like delve and tapestry are more common than in casual first-person speech. A widely cited 2024 study tracking word frequency in academic papers found usage of delve spiked the same year ChatGPT launched, which is strong evidence the word's rise is tied to AI-assisted writing rather than a natural trend.
There is no confirmed LinkedIn algorithm rule that penalizes specific words. The effect is more indirect: generic, tell-heavy posts read as less personal and less specific to human readers, who scroll past them faster and comment on them less, and lower engagement in the first hour is what actually caps a post's distribution. The words are a symptom of generic writing, and generic writing gets less engagement regardless of the platform.
Yes, with one caveat: swapping individual words is not enough if the underlying structure is still generic (a vague opener, hedge clauses, a low-effort closing question). Editing works best when it targets both the vocabulary and the structure: add a specific number or moment near the top, remove hedges, and close with a specific question. A tool that starts from your own writing samples rather than a blank prompt will need far less of this editing to begin with.
Read the draft out loud. If a sentence is something you would never actually say to a colleague, or if the post opens with a scene-setting line that could describe literally any topic, it likely reads as generic. Running it against the banned-word list above and checking whether the closing line asks something specific are the two fastest manual checks, both more reliable than a public AI detector score.
Replace vague intensifiers with the specific, concrete outcome they are standing in for. Instead of 'unlock the power of automation', say what the automation actually does and by how much (for example, 'cuts report time from 20 minutes to 90 seconds'). Instead of 'leverage your network', say what action you actually took. The rewrite column of the lookup table above gives a plain alternative for each entry.