ChatGPT Words to Avoid: 40 Phrases That Give You Away

There is nothing wrong with the word "delve." It sat quietly in the dictionary for six hundred years bothering nobody. Then ChatGPT arrived, and within two years a study of 15 million biomedical abstracts found "delve" appearing 17 times more often than its historical baseline. The word didn't change. What it signals did.

I cut these words out of client drafts for a living, and here's the thing most lists get wrong: no single word on this page proves anything. The tell is density. One "crucial" is writing. Four "crucials," two "landscapes," and a "tapestry" in an 800-word post is a fingerprint.

Why ChatGPT keeps reaching for the same words

Language models only ever predict the most statistically likely next word. They then get tuned by human raters who reward the most polished, confident-sounding answers. What you get is vocabulary regression to the mean: not the right word for your sentence, but the most rewarded word across millions of other sentences. How widespread is this? The study's authors put it at 13.5 percent of 2024 biomedical abstracts, minimum. A few subfields hit 30. And the signal words traveled with it, going from 47 per 10,000 papers before ChatGPT to 224 after.

So these words aren't "wrong." They're crowded. Every one of them now carries a faint watermark, and readers have learned to smell it.

The 40 words and phrases

Verbs that do too much work (1–8)

Delve: the poster child. "Explore" or "look at" both survive scrutiny better.
Embark: nobody embarks on a blog post. You start it.
Foster: fine in a grant application, suspicious everywhere else.
Leverage: as a verb meaning "use," it's the oldest corporate tell there is. AI made it worse.
Harness: horses, yes. "The power of" anything, no.
Underscore: the PubMed study flagged this one specifically. "Show" works.
Streamline: usually attached to a process nobody described in the first place.
Bolster: a word almost no one says out loud, which is exactly the problem.

Adjectives that inflate (9–16)

Comprehensive: especially glued to "guide."
Crucial: AI's favorite intensity setting. Most crucial things are just relevant.
Pivotal: same disease, fancier suit.
Robust: outside of statistics and coffee, treat it as a flag.
Seamless: nothing is seamless. Users know this. Writers should too.
Transformative: if everything transforms, nothing does.
Meticulous: another one the abstract study caught spiking. Real people say "careful."
Ever-evolving: usually welded to "landscape," which is coming up next.

Nouns and metaphors nobody asked for (17–24)

Tapestry: the single most mocked AI word on the internet, for good reason.
Landscape: "the landscape of X" is filler pretending to be scope.
Realm: unless you're writing fantasy, stay out of the realm.
Testament: "a testament to" adds zero information, always.
Synergy: died in corporate meetings, resurrected by chatbots.
Beacon: things keep "standing as beacons" in AI copy. Nothing stands as a beacon.
Treasure trove: a phrase that has never once improved a sentence.
Journey: your reader is not on a journey. They're reading a paragraph.

Transitions and filler (25–32)

Furthermore: humans write "also," or just start the next sentence.
Moreover: same. Stacked transitions are a structural tell, not just a word one.
Additionally: the third sibling. AI drafts often use all three in one piece.
In conclusion: if the reader can't tell it's the conclusion, the ending failed anyway.
It's important to note: then just note it.
When it comes to: pure throat-clearing. Delete it and the sentence still works.
In today's digital age: the most famous AI opener of all. Instant credibility loss.
At the end of the day: a spoken cliché AI mistakes for warmth.

Sentence patterns, the deeper tell (33–40)

"Whether you're a beginner or a seasoned pro…": the fake-inclusive opener.
"Not only X, but also Y": once per essay is style, three times is a template.
"From X to Y to Z": AI's way of gesturing at breadth without providing it.
"Serves as a": things can just be. They don't need to serve as.
"Plays a crucial role in": double flag: the pattern and the adjective.
"Let's dive in": the enthusiastic cousin of "delve."
"In the world of…": an opener that locates nothing.
"…, making it an ideal choice for…": the participial tail. AI hangs one of these on every third sentence.

One of these is fine. Five is a pattern.

Editors were cutting most of these words long before ChatGPT existed, because they're padding. What changed is the stakes: readers, teachers, and clients now pattern-match. A hiring manager reading a cover letter with "I am excited to leverage my transformative skill set on this journey" doesn't need a detection tool. The vocabulary does the detecting.

The reverse matters too. If your natural voice includes "robust" because you're a data analyst, keep it. Deleting every flagged word from writing you actually wrote is over-correction, and it sands off your voice. The goal is that no reader hits three of these in one screen of text.

How to actually clean a draft

A find-and-replace pass is the floor, not the fix, because the deeper tells are structural: uniform paragraph lengths, transitions stacked at every seam, that participial tail on sentence after sentence. Here's the pass I run on drafts that started life in ChatGPT:

Read one paragraph aloud. Anywhere you'd never say the words, rewrite in the words you'd say.
Cut every transition word at the start of a sentence, then re-add only the ones the logic actually needs. It's usually one in five.
Hunt the patterns, not just the words: two "not only" constructions or two participial tails is already too many.
For a draft that's too far gone to hand-edit, run it through a free AI humanizer that rewrites sentence structure rather than swapping synonyms, then do the read-aloud pass on the output.

You can also stop the problem upstream. ChatGPT's custom instructions field ("Customize ChatGPT" in settings) accepts a standing rule like "avoid these words and their derivatives," followed by your blocklist. It won't catch everything, the model drifts back toward its favorites in long outputs, but it cuts the cleanup work roughly in half on the drafts I've compared. Claude and Gemini honor the same kind of instruction in a project or system prompt.

Worth knowing: modern detectors like Turnitin don't just count buzzwords, they measure how predictable your sentences are as a sequence. That's why swapping "delve" for "explore" barely moves a score, and it's also why even Grammarly-polished human writing sometimes gets flagged. Vocabulary is the tell humans notice. Structure is the tell machines notice. You need to fix both.

What this list is actually for

Cut these words because they're empty, not because a robot might have written them. Every phrase above survives on borrowed importance: it sounds like meaning without adding any. That was true when consultants wrote this way in 2005, and it's true now that a model does it at scale.

And if the worst has already happened, a paper or post flagged as AI that you genuinely wrote, vocabulary lists won't help you after the fact. There's a specific evidence process for proving you wrote it, and it works better than arguing about word choice ever will.