AI writing tells
The recognisable habits of AI-generated text, such as stock phrases, tidy groups of three, empty enthusiasm and vague claims, that make writing feel generic and erode readers' trust.
Also called: AI slop · sounds like ChatGPT · AI tropes · generic AI voice · AI-isms
Connections
Understand first
Ideas this one builds on.
Related
Often used together or easily confused.
Blueprints that practise it
Resources that teach it
Competency
Find ai writing tells on the commons map ↗
You may know it from
- business writing corporate boilerplate Phrases that fill space without saying anything specific, which readers learn to skim past.
- communication spotting a form letter
In products
The same idea, under different names in different tools.
| Provider | What they call it |
|---|---|
| Every | draft-review-kit guardrails reviewer (Checks a draft for AI-sounding phrasing and evidence gaps; see fld-every-draft-review-kit.) |
| Peter Yang | no-ai-slop skill (See fld-no-ai-slop.) |
In plain words
Readers have learned to spot AI-written text. The giveaways vary by tool and change over time, but common ones include stock openers and closers, lots of grand but vague words, lists that always come in threes, sentences built around a contrast that wasn’t needed, cheerful enthusiasm about everything, and paragraphs that summarise what was just said.
The bigger problem underneath is generic content. AI fills gaps with safe, average phrasing because it doesn’t know your specifics. When a reader senses that, they trust the message less, even if it’s accurate.
Fixing it is mostly editing for specifics. Replace general claims with the real number, name or example. Cut sentences that could appear in anyone’s email. Read it aloud and change anything you would never say. Giving the AI samples of your own writing up front prevents much of this.
Example
An AI-drafted fundraising appeal opens with a sweeping line about “making a difference in today’s world.” The writer replaces it with the real story of one family the program helped last month, cuts the closing summary, and the appeal reads like her.
How it connects
- A side effect of how a large language model defaults to likely phrasing.
- Examples in prompts are the best prevention.
- Reading for tells is a form of verification for voice and substance.
Try it
Take a recent AI draft and underline every sentence that could appear unchanged in someone else’s email. Rewrite one of them with a detail only you know.
Read this card as plain Markdown: /concepts/con-ai-writing-tells.md. Know a parallel from your own field, or a product that implements this? Add it.