AI Words to Avoid: The Words and Phrases That Give Away ChatGPT Writing

What Are AI Words to Avoid, and Why Do They Matter?

AI words to avoid are the words and phrases that large language models reach for so consistently that they've become a kind of fingerprint. None of these words are wrong in isolation — "delve" is a real word, and "moreover" is grammatically fine — but when several of them cluster in the same paragraph, the writing starts to sound assembled rather than written. Readers pick up on this before they can name it; a piece of copy just feels flat, or a cover letter reads like it could have been written for anyone. Editors and hiring managers who review dozens of submissions a week notice the pattern even faster, because they see the same handful of words recur across unrelated writers. That's the practical reason this list matters: it isn't about satisfying a detector, it's about the fact that repetitive, generic-sounding vocabulary reads as generic-sounding writing, whether or not AI was involved. Search engines have quietly pushed in the same direction — content that sounds templated tends to underperform content with a specific point of view, partly because it mirrors thousands of other pages built on similar model outputs. Most people who go looking for a list of AI words are trying to solve one of these problems directly: a flagged assignment, a rejected pitch, or just a nagging feeling that a draft doesn't sound like them anymore.

The words on this list aren't banned vocabulary. They're a signal that a sentence was built from the most statistically likely choice at every step, rather than the choice a specific person would actually make.

Why Do AI Models Keep Reusing the Same Vocabulary?

Language models generate text one token at a time, choosing whichever word is most probable given everything written so far. Across billions of training examples, certain words end up statistically favored for common rhetorical jobs — "delve" for exploring a topic, "moreover" for adding a point, "robust" for describing something well-built — and the model reaches for them by default because they carry low risk of sounding wrong. The tuning process most chatbots go through afterward, where human reviewers rate responses as helpful or unhelpful, tends to reward measured, formal-sounding phrasing over blunt or idiosyncratic wording. That pushes the model even further toward a narrow, safe vocabulary rather than away from it. The result is that two people asking a chatbot completely different questions — one about marketing copy, one about a research summary — often get answers that lean on the same handful of transition words and descriptive adjectives, because the underlying selection process is the same regardless of topic. Newer models trained partly on earlier AI output tend to inherit and reinforce this same set of common AI words, so the pattern hasn't faded as the technology has improved — if anything, the most common AI words and phrases have become more consistent across tools, not less. Once you've read enough AI output, the repetition becomes obvious: the vocabulary is consistent in a way that human writing, with its personal habits and inconsistencies, rarely is.

What Are ChatGPT's Favorite Words and Most Common Phrases?

Certain words show up in ChatGPT output so often that they've become shorthand for "this was probably generated." Call them ChatGPT favorite words, common ChatGPT words, or just common AI vocabulary — the label doesn't matter, the pattern does. A few of them were fairly uncommon in everyday writing before chatbots existed and now appear constantly in AI-assisted drafts, cover letters, and blog posts submitted for review. The phrase-level patterns are just as recognizable as the word-level ones: "I hope this helps," "it's important to note that," "as an AI language model," and "let's break this down" are common ChatGPT phrases that show up regardless of subject matter, because they're the model's default way of framing an answer rather than content specific to your request. When several of these words and phrases cluster together in one piece of writing, that clustering — not any single word — is what makes text read as AI-generated.

1. Delve — used almost exclusively in AI text to mean "look into" or "explore," rarely appearing in casual human writing
2. Boast — as in "the product boasts an impressive range of features," a formal substitute for "has" or "includes"
3. Meticulous / meticulously — a default intensifier for describing careful work, regardless of context
4. Intricate — applied to almost anything with more than one moving part, from designs to arguments
5. Vibrant, tapestry, realm — abstract, slightly poetic nouns and adjectives that show up in descriptions with no concrete subject
6. Underscore, showcase, foster, bolster — formal verbs favored over plainer alternatives like "highlight," "show," "support," or "strengthen"
7. Utilize — chosen over "use" even though the two words mean exactly the same thing in nearly every sentence

What's on the Full List of Common AI Words and Phrases?

Beyond the words tied specifically to chatbots, there's a broader list of AI words that shows up across generated content of every kind — blog posts, emails, product descriptions, even school essays. Put together, these are the most common AI words and phrases people run into once they start paying attention, and they tend to fall into a few recognizable categories. Once you can name the category, spotting a new example gets easier, and common AI phrases stop feeling random and start feeling like a template you can recognize on sight.

1. Transition words used at nearly every paragraph break: moreover, furthermore, additionally, in addition, on the other hand, that said
2. Inflated adjectives applied to ordinary things: robust, seamless, comprehensive, unparalleled, cutting-edge, game-changing
3. Vague power verbs standing in for simpler ones: leverage, streamline, empower, harness, unlock, elevate
4. Abstract nouns with no concrete referent: realm, landscape, tapestry, myriad, plethora, paradigm
5. Hedging openers that pad a sentence without adding meaning: it's worth noting that, it's important to note, needless to say
6. Closing phrases that wrap up a point without saying anything new: in conclusion, overall, in summary, at the end of the day

What Common Phrases Does AI Use in Transitions and Endings?

So what are common phrases that AI uses beyond individual vocabulary? Word choice is only half of the pattern — AI-generated text also leans on a predictable set of structural phrases at specific points in a piece, especially at paragraph openings and article endings. Sentences frequently open with "when it comes to," "in today's fast-paced world," or "in the ever-evolving landscape of," phrases that announce a topic without saying anything specific about it. Arguments get connected with "not only… but also" far more often than natural speech would use it, and claims get inflated with "plays a crucial role" or "plays a pivotal role" regardless of how central the thing being described actually is. Endings are the most consistent tell of all: "in conclusion," "overall," and "in summary" show up at the close of AI-written sections at a rate that would be unusual in edited human prose, where writers tend to end on a specific point rather than a generic wrap-up label. None of these phrases are wrong on their own — the issue is frequency and placement. A human writer might use "in conclusion" once in a long document; AI-generated text tends to use a structurally similar closing phrase at the end of nearly every section, because the model follows the same template each time it reaches a section boundary.

Structural repetition is often a stronger tell than any single word choice — the same closing phrase showing up at the end of every section is a pattern a careful human editor would catch and vary, but a model repeats by default.

How Do You Replace Common AI Words to Avoid With Natural Language?

Fixing this is mostly a matter of substitution, not rewriting from scratch. Once you've marked the common AI phrases to avoid in a draft, editing it down to plainer language usually takes less time than the original writing did.

1. Read the draft out loud — words like "utilize," "leverage," and "delve" tend to stand out immediately when spoken, because nobody talks that way in conversation
2. Swap inflated verbs for the plain version: "utilize" becomes "use," "leverage" becomes "use" or "rely on," "foster" becomes "build" or "encourage"
3. Replace abstract nouns with a concrete detail: instead of "in the realm of customer service," name the actual thing — "in customer support tickets" or "on support calls"
4. Cut hedging openers entirely rather than replacing them — "it's worth noting that the tool is fast" just becomes "the tool is fast"
5. Vary or delete formulaic closings — replace "in conclusion" with a specific final point, or remove the label and let the last sentence stand on its own
6. Check for repeated transition words across paragraphs — if three paragraphs in a row start with "moreover" or "additionally," rewrite at least two of the openers
7. Read the edited version against the original and ask whether a specific person, not a general authority, seems to be speaking

Does Cutting These Words Actually Help You Pass AI Detection?

Editing out AI words to avoid makes writing sound more natural, but it's worth being honest about what word-level edits can and can't do against AI detection tools specifically. Detectors like the one built into NotGPT don't just scan for a blocklist of words — they analyze sentence-level patterns such as perplexity and burstiness, which measure how predictable your word choices are and how much your sentence lengths vary. Swapping "utilize" for "use" throughout a document that still has uniform sentence lengths and a template-like structure will make the writing read better without necessarily changing its detection score much. That said, the two problems tend to travel together: text with heavy AI vocabulary usually also has the flat, low-variation sentence structure that detectors flag, so cleaning up the words is often a natural first step toward fixing the underlying pattern. If you're editing something before it gets submitted or published, running it through an AI text detector first shows exactly which passages read as generated, and a tool like NotGPT's Humanize feature can rework flagged sections while you handle the vocabulary changes by hand. Used together, word-level editing and a detection check catch different halves of the same problem. It's also worth remembering that no detector, including NotGPT's, claims perfect accuracy — scores are a probability, not a verdict, so treat a high score as a prompt to review specific passages rather than proof that a document was AI-written.