Tell glossary
The AI writing tells underslop detects
One concept per page, with the measurement behind it.
A tell is a structural pattern that shows up far more often in machine text than in human text. underslop scores a consensus set of them and reports a second, contested set it refuses to count. Each page below covers one tell: what it is, how often we measured it, why readers react to it, and how to fix it without flattening the sentence.
The glossary
- Negative parallelism
- The reframe that denies a plain reading to assert a grander one is the most-cited AI writing tell. What it is, how often models produce it, and how to fix it.
- The "not only... but also" frame
- The additive frame that stacks a concession onto a grander claim is a high-frequency AI tell. What it is, how often models use it, why it reads as generated, and the fix.
- The em-dash chain
- A single em dash is not an AI tell. A chain of them, several in a short span, is. What our logs show, why readers flag density, and how to fix it.
- Assistant tells
- Validation openers and engagement sign-offs are the clearest proof text came straight from a chatbot. What our logs show, why they stand out, and the fix.
- The bold-header list
- Turning prose into bullets that each start with a bold colon label is a high-frequency AI formatting tell. What our logs show and how to fix it.
- Emoji as section decoration
- Using an emoji to decorate a heading or bullet is a formatting tell. What our logs show, why it reads as generated, and how to fix it.
- The throat-clearing pivot
- The colon-label pivot that announces a revelation before delivering one is a tell grounded in our own model logs. What it is and how to fix it.
- Vague attribution
- Citing research or experts with no actual source is a recognized AI tell. What it is, why readers distrust it, and how to fix it.
- The frequency-ranked checklist
- Every AI-writing checklist lists the same dozen signs with no sense of which matter. Ours ranks them by frequency across twelve models in our own logs.
- The three-slot response macro
- In one of twelve models we logged, ChatGPT replies followed a three-slot macro that recurred across five unrelated topics, not any single sentence shape.
- Lexical fixes vs. behavioral reflexes
- Most advice for sounding less like ChatGPT targets vocabulary already absent from our model logs, while the near-universal reflexes go untouched.
- Five sliders, not an adjective
- Asking ChatGPT to write like you swaps a few words on the same shape. Running the same draft through five calibrated sliders shows what actually changes.
- The em-dash density ceiling
- A single em dash never trips underslop's scorer. What does: two or more inside the same 150-word stretch. Here is the number, and what a chain looks like.
- Slop, defined as a density
- 'Slop' gets used as an insult with no fixed meaning. underslop defines it as a density: scored tells per 100 words, from the linter that grades every page.
- The clipped one-line beat
- AI-writing advice treats the one-line paragraph as a universal chatbot habit. Our own model logs show it in three of twelve models, not all twelve.
- The graded checklist
- Most 'AI writing style guide' pages are essays you can't check. This is the checklist underslop runs, every entry graded, some tracked but unscored.
- 'Let's dive in' and 'deep dive'
- Two phrases get treated as the same red flag. One is a chatbot handoff we measured directly. The other is workplace speech our own research cleared.
- The rule of three (tricolon)
- Tricolon overuse showed up in a minority of models. Our social research found the pattern indefensible, so underslop tracks it but never scores it.
- The bold colon lead-in: one tell, two forms
- One model in our logs opened sentences with a bold colon label 35 times in 3,500 words. Same tell as bold-header-list, at its most concentrated.
- Slush-pile sameness and the comp-title pitch
- A novel has no headings, no bullet points, often no em-dash chains. The tells that survive are corpus-level: editors comparing hundreds of submissions.
- The closed-connective stack ("in conclusion," "furthermore," "moreover")
- Twelve models, zero uses of "in conclusion," "furthermore," or "moreover" as connective stacking. The top AI cliché mostly flags human essay writers.