AI Vocabulary Words to Avoid

July 19, 2026· patterns

Certain words are not bad English. They are overrepresented in model output, which makes them statistical beacons. Avoiding them is not a game of synonym roulette. It is a way to escape the default voice baked into training data.

High-frequency AI favorites

You have seen these: delve, tapestry, landscape (metaphorical), crucial, vibrant, foster, underscore, showcase, intricate, interplay, testament. They appear in essays, emails, and documentation with eerie regularity—not because they are precise, but because they are safe.

Safe for a model means high probability. Safe for a reader means forgettable.

Before: This policy underscores the intricate interplay between compliance and innovation in today’s regulatory landscape.

After: The policy explains which experiments need legal review before we ship them.

Why word choice matters to detectors

Classifiers learn co-occurrence. “Delve” plus “tapestry” plus “rich” in one paragraph is a fingerprint. A single unusual word is nothing. A cluster is a profile.

Humans react similarly, just without percentages. The paragraph feels like a template.

Replace patterns, not single tokens

Swapping “delve” for “explore” and leaving everything else intact often fails. The surrounding syntax may still be generic: passive openings, inflated significance, promotional verbs.

Better approach: rewrite the sentence around a concrete noun and a plain verb.

Tables are shortcuts. Your real test is whether a specialist would use the word in speech.

Genre matters

A word that is fine in a novel may be suspicious in a bug report. “Tapestry” in literary criticism might pass; in a sprint retrospective it will not. Calibrate to room, not to a global ban list.

I still use “crucial” when something actually breaks without it. The goal is density control, not purity.

Editing workflow

Highlight your draft’s adjectives and nominalizations. Circle verbs like “underscore” and “highlight” that comment on importance instead of describing action. For each circled word, ask: what is the physical or observable version of this claim?

Run a pass with REhume if you want automated suggestions, then accept or reject with context. Automated tools catch clusters; you keep meaning.

Building a personal lexicon

Notice words you overuse in your own AI-assisted drafts. Keep a short private list on a sticky note. Over time your defaults shift, and detectors have less generic material to grab.

AI vocabulary is a pattern, not a morality tale. Write so a colleague recognizes your thinking—not the median of the internet.

Density, not prohibition

The issue is concentration. One “landscape” in a long essay is invisible. Five in three paragraphs is a tell. Track density per page, not per word.

I run a find-all for my personal trigger list before publishing. If counts are high, I rewrite the worst paragraph first. Momentum matters more than perfectionism.

Teaching models your lexicon

In prompts, specify allowed jargon for your field and ask for plain verbs otherwise: “Use our product names; avoid words like delve, tapestry, crucial unless quoting.” Prompting does not eliminate cleanup, but it reduces it.

After REhume

Automated passes may replace one AI favorite with another equally generic synonym. Read for clusters, not single-token swaps. If the voice still feels imported, add a sentence only you could write—a constraint from your project, a date, a mistake you fixed.

Vocabulary tells fade when the draft carries real information. Words are the last layer, not the first fix.