AI Words to Avoid: 218 Terms Ranked by Evidence (2026)

Every list of AI words to avoid makes the same silent mistake. It puts delve next to leverage next to tapestry and tells you to cut all three, as if they were the same kind of claim. They aren’t.

Delve was measured. Published studies found it disproportionately frequent in specific model outputs compared with a human baseline. Tapestry is perceived: readers say they associate it with AI writing, which is real information about how you’ll be read, but nobody measured its frequency. And leverage is neither. It’s a word an experienced editor would question for vagueness, and the honest thing to say is that it was a problem in corporate writing twenty years before anyone typed a prompt.

Three different claims. Three different levels of confidence. One list that pretends they’re identical, which is why running it as find-and-replace makes writing worse.

So this reference does something the others don’t. It grades every entry by the evidence behind it, using a three-tier system I’ve come to call the Choudhury Tiers, and it tells you when to keep the word as well as when to question it. There are 218 entries: 117 words and word families, 83 phrases, and 18 structural patterns, checked through September 2026, with sources for each. The full sortable workbook is FREE to download, with the source URL and evidence scope for every entry. The complete list is also on this page below; no email required.

One disclosure, because I’d expect it of anyone else: I built this reference with AI research assistance, then reviewed the sources and the reasoning. The tiers, the editorial calls, and the mistakes are mine.

The Choudhury Tiers

The Choudhury Tiers grade AI-associated vocabulary by evidence strength: Tier C for words measured as over-frequent in model output by a published study, Tier H for words readers report associating with AI, and Tier E for words an editor would question on quality grounds without claiming they mark AI authorship.

Tier C, corpus association. A study compared model output against a specified human baseline and found the word disproportionately frequent. This is the strongest evidence available, and it’s still narrow: it applies to the models, prompts, and genres that study used. A word that spiked in scientific abstracts from one model in 2024 may not spike in news writing from a different model in 2026.

Tier H, human-reader association. Readers, or researchers reporting readers’ explanations, or editors, have identified the word as something they associate with AI. This is a perception, not a frequency. It’s worth knowing because your readers carry those perceptions, but it isn’t a validated test of anything.

Tier E, editorial caution. Worth inspecting for vagueness, inflation, cliché, or a borrowed voice. Not claimed as an AI marker. Most of the words that trigger a reader’s “this sounds like marketing” reflex live here, and many of them predate language models entirely.

Why the tiers matter more than the words: one of the corpus studies behind Tier C found conservationist, firsthand, and introspective over-represented in model output. Those are ordinary English words. They appeared on the list because a narrow research sample happened to contain no human uses of them, and a zero in a small sample is a fact about the sample, not about human writers. Any list that hands you those three as “AI words” without the tier label is teaching you to distrust perfectly good English.

Tier C: the 38 measured words

Delve, underscore, showcase, boast, intricate, intricacy, garner, emphasize, groundbreaking, advancement, align, comprehend, surpass, realm, additionally, revolutionize, revitalize, captivated, conservationist, streamline, firsthand, personalize, introspective, importance, resilience, dedication, commendable, crucial, invaluable, meticulous, notable, noteworthy, pivotal, potential, significant, strategically, suggest, strategy.

Read that list again and notice how much of it is unremarkable. Importance. Potential. Significant. Suggest. Strategy. A May 2026 LREC paper specifically identifies suggest, additionally, and strategy as statistically distinctive across six model families. That finding does not mean you should stop using the word strategy. It means model preferences aren’t confined to ornate words like tapestry, and it means any test based on spotting fancy vocabulary will miss most of what’s actually going on.

The editing question for Tier C isn’t “is this word present.” It’s “is this word doing the job it claims to do.”

❌ This underscores the pivotal importance of a meticulous strategy.
✅ Without a written checklist, the team missed two approval steps last quarter.

❌ The findings suggest a significant advancement in the field.
✅ The new method cut processing time from four hours to forty minutes on the same hardware.

I’d question the first pair immediately, because it announces significance without explaining it. I would not question statistically significant, align the mounting holes, or a firsthand account. Same words, different jobs.

Tier H: the 34 perceived words

Tapestry, landscape, navigate, elevate, empower, harness, enhance, foster, seamless, robust, transformative, profound, vibrant, multifaceted, nuance, comprehensive, paramount, moreover, meaningful, authentic, curated, crafted, essential, critical, explore, journey, resonate, embrace, transcend, whimsical, valuable, tailored, deeper, ensure.

These are the words that make readers say “this sounds like ChatGPT.” Whether or not a model actually overuses them, your reader thinks it does, and for copywriting that perception is what matters.

The useful unit here isn’t the single word. It’s the cluster.

A robust, seamless solution that empowers you to navigate an ever-changing landscape.

Four Tier H words in one sentence. But the problem isn’t the word count. The sentence withholds every piece of information the reader needs: what the solution does, what it works with, and what becomes easier. The words are there because the information isn’t.

So instead of swapping robust for powerful, ask what the word is standing in for:

Every one of those questions produces a better revision than a synonym would. And this family is where non-native writers get hit hardest, because brochure register is what a lot of business-English instruction actually teaches. It isn’t your error. It’s an inherited habit, the same one I described in unlearning school English.

Tier E: the 45 editorial cautions

Unlock, unleash, leverage, utilize, facilitate, optimize, synergy, holistic, dynamic, innovative, cutting-edge, state-of-the-art, next-generation, future-proof, game-changing, unprecedented, unparalleled, effortless, intuitive, frictionless, bespoke, unwavering, unwaveringly, beacon, cornerstone, testament, interplay, ecosystem, paradigm, spearhead, bolster, embark, nestled, bustling, breathtaking, quietly, legible, load-bearing, texture, tender, intentional, grounded, lived, sustainable, actionable.

This tier needs the strongest qualification, so here it is plainly: these are not proven AI tells. They’re words I’d inspect when they inflate a claim, hide a mechanism, or import a voice that doesn’t belong to the writer.

I’d question future-proof because it makes an unusually broad promise. I’d question actionable when no action follows. I’d question quietly when it adds atmosphere and no meaning. But optimize, ecosystem, load-bearing, sustainable, texture, and tender can be exactly the right words, and a list that removed them automatically would be actively harmful.

❌ Unlock seamless energy independence with a robust, cutting-edge solution designed to empower your everyday journey.
✅ Keep essential devices powered during an outage. Choose the battery capacity and output your setup needs.

The second version still needs a real product behind it. It’s better because it names a use and a decision, not because it contains detector-safe vocabulary.

The 83 phrases, by what they do

Phrases fail by function, not by vocabulary, so the workbook groups them that way. The families, with the tier label and one example each:

Generic openings (E). In today’s fast-paced world; In an ever-evolving landscape; Now more than ever; In the realm of. Cut the interchangeable setting and start with the particular event. “In the digital age, businesses must adapt” could open ten thousand articles. “Customers now finish the application on their phones” opens one.

Announcing instead of starting (H). Let’s dive in; This article explores; When it comes to; It is important to note; In conclusion. Some of these are aging tells, historical rather than current, but readers still flag them. Keep navigation that genuinely helps in a long document.

Declaring importance without explaining it (H). Plays a pivotal role; serves as a testament to; underscores the importance of; marks a significant shift; paves the way for. The fix is always the same: explain the consequence instead of asserting the weight.

Generic promotional promises (E). Unlock your potential; take X to the next level; seamlessly integrates; a game-changer for; drive meaningful impact; deliver actionable insights. For marketing copy, this is the family to review hardest. A headline doesn’t need a specification, but it needs a recognizable benefit or a real distinction. Replacing information with enthusiasm is the failure.

Repeated contrast as revelation (H). It’s not X, it’s Y; Not just X, but Y; No X. No Y. Just Z. This one deserves attention in 2026 specifically, because it’s become the default shape of every “insight.” It remains legitimate rhetoric; the problem is a page where every paragraph corrects an invented X with a grander Y.

Performing candor (E). Here’s the thing; Let’s be honest; The truth is; Read that again; Let that sink in; No fluff. I wouldn’t ban conversational language. I’d question repeated announcements that the next sentence is unusually honest or important. The plain sentence usually does more work than the drumroll.

Automatic intimacy (E). A gentle reminder; hold space for; you’re not alone; you’ve got this; honor your journey. Fine in a supportive conversation. The question in professional writing is whether the relationship and subject have earned this degree of closeness.

Borrowed authority (H). Experts agree; research shows; studies suggest; a growing body of evidence. Keep these when they introduce identifiable evidence. And do not remove suggest to sound more confident. Sometimes the uncertainty is the most accurate word in the sentence.

Chat residue (H). Certainly! Here is; I hope this helps; Let me know if you need. Remove when it has survived into published copy. It’s fine inside an actual conversation.

The 18 structural patterns a word list can’t see

Vocabulary is the surface. Research comparing matched human and model passages finds the deeper differences in structure: noun-heavy constructions, present-participle clauses, the shape of paragraphs. The workbook lists eighteen; these are the ones I check first.

Repeated negative parallelism (H). Every paragraph corrects an invented X with a grander Y.

Decorative trailing -ing clauses (C/H). A fact, then “highlighting its importance,” adding nothing the fact didn’t already carry.

Serial three-part lists (H). Every benefit arrives as three balanced items regardless of how many there actually are.

Constant micro-sentences (H). Ordinary prose chopped into theatrical fragments. Sentence boundaries should serve meaning, not drama.

Identical paragraph construction (E). Claim, example, uplifting slogan, repeat.

Noun-heavy abstraction (C). Actors and actions vanish behind “the facilitation of implementation.”

Automatic challenges-and-future endings (H). Every topic closes with generic obstacles and an optimistic horizon, whatever the evidence said.

Praise after every fact (H). Each statistic immediately called remarkable or transformative.

Missing conditions and trade-offs (E). Everything delivers benefits; nothing has a cost, limit, or dependency. This one is the quietest and, in my experience, the most reliable.

I covered the rhythm version of this, the flat sentence-length pattern, at length in sentence length and non-native writing. It’s the structural tell a word list is least equipped to catch.

What does this mean for AI detectors

Short version: nothing you’d want it to mean.

There is no defensible detector-specific blacklist. GPTZero’s own documentation describes contextual learned representations with overlapping human and AI distributions, not a rule where one delve equals a fixed penalty. Removing these 117 words will not produce a predictable score, and I did not run commercial detector tests for this reference, so it has no measured pass rate.

Worse, editing can move different detectors in opposite directions. An August 2026 preprint examined over 135,000 pairs of original and professionally edited academic manuscripts across 13 research detectors, and found that professional human editing raised scores on some detectors and lowered them on others. That’s a preprint, one author is affiliated with the editing provider, and it concerns research detectors rather than commercial products. But it supports what I documented with real data in the false positive problem: style-based classification is not a reliable authorship test.

For ads, subject lines, and anything short, be more careful still. Turnitin’s own documentation requires at least 300 words of long-form prose and warns that short-form output shouldn’t be interpreted as a verdict.

Edit for readers. The score is not the goal.

How to actually use this

Four passes, in this order.

Check truth and residue. Remove accidental chat language. Verify every quotation, citation, specification, and personal claim.

Replace unearned praise with information. For every Tier H and Tier E word, ask what it’s standing in for. Seamless with what? Robust against what? Answer the question in the sentence.

Inspect repeated construction. Look for a whole page of contrasts, identical paragraph endings, ornamental conclusions, theatrical fragments.

Preserve the voice. Keep technical vocabulary, necessary uncertainty, deliberate rhythm, and legitimate rhetoric. A human-writing standard cannot mean “plain, short, and unadorned,” or half the best writers alive would fail it.

Run all of this in the naturalness pass, never while you’re still fixing grammar, for the reasons in the two-pass edit.

And if you’re using a model to help with the pass, here’s the instruction that embodies everything above:

Review this draft for generic AI-associated phrasing without treating individual words as proof of authorship. Flag unsupported praise, vague claims, repetitive contrasts, unnecessary signposting, decorative metaphors, and borrowed tone. Preserve factual meaning, technical terms, necessary uncertainty, and my established voice. Replace flagged wording with specific, accurate language. Do not invent facts, personal experiences, errors, or casual slang. Explain only changes that materially improve the writing.

The honest limits

The list ages, and I’ll retire entries. These words spiked because of what models were trained on and how they were tuned. Both change. Wikipedia’s own editorial guidance already treats some signposting phrases as historical rather than current tells. A 2026 preprint found that of 27 word-and-part-of-speech items associated with earlier scientific writing, 22 transferred to news writing and five did not. Recycling an old list with a new year in the title is how most of these pages are made, and it’s inadequate. I’ll add evidence and remove weak assumptions rather than only ever adding.

Specificity isn’t proof of humanity either. A model can imitate concrete detail. But specificity is a far more useful editing target than manufactured roughness, and the writers people actually read use three-part lists, em dashes, long cumulative sentences, and “not X, but Y” constantly. What separates them is that every device belongs to a particular thought. A phrase can be familiar and still earn its place. A sentence can avoid every word here and still say nothing, which is the deeper problem I worked through in how to edit AI copy so it sounds human.

My strongest recommendation, and the one that will outlast every word on this page: avoid language that substitutes for a thought, a proof, or a useful detail. That’s a more durable standard than avoiding every word a model also happens to use.

Frequently Asked Questions

What are the Choudhury Tiers?
The Choudhury Tiers grade AI-associated vocabulary by evidence strength: Tier C for words measured as over-frequent in model output by a published study, Tier H for words readers report associating with AI, and Tier E for words an editor would question on quality grounds without claiming they mark AI authorship. The system exists because most AI word lists treat all three kinds of claim as identical.

Which AI words should I avoid first?
For professional writing in 2026, the shortlist worth checking first is delve, underscore, tapestry, realm, pivotal, boast, showcase, intricate, seamless, robust, transformative, elevate, empower, harness, foster, enhance, groundbreaking, multifaceted, profound, vibrant, meaningful, authentic, journey, navigate, and additionally. That is an editing shortlist, not a ranking of detector triggers, and each word’s use matters more than its presence.

Is delve really an AI word?
Delve is a Tier C word: published research found it disproportionately frequent in specific model outputs compared with human writing in the same genre. That association is real but narrow, tied to particular models and text types. It does not mean a human using delve wrote with AI, and removing it does not change a detector score in any predictable way.

Does removing AI words help me pass AI detectors?
No, not reliably. Detectors use contextual statistical models rather than word blacklists, and a 2026 study of over 135,000 edited manuscripts found professional editing raised scores on some detectors and lowered them on others. Remove weak words because they weaken writing, not to chase a score.

Why does the list include ordinary words like strategy and suggest?
Because corpus research found them statistically distinctive in model output, which shows that model preferences are not confined to ornate vocabulary. It does not mean those words should be banned. It means a test based only on spotting fancy words will miss most of what’s actually distinctive about generated text.

Where to go next

👉🏼 For what to do after you’ve questioned the words, see how to edit AI copy so it sounds human.

👉🏼 For prompts that prevent these words at the source, see 15 ChatGPT prompts for non-native writers.

👉🏼 For the rhythm tell no word list catches, see sentence length and non-native writing.

👉🏼 For why detector scores are the wrong target, see the false positive problem.

Grade the evidence. Question the word. Keep the voice.