ChatGPT Hyphen

The character "—" AI LLM chat models are constantly using is called the em dash (Unicode U+2014). It's a punctuation mark about the width of a capital letter "M" (hence the name), longer than a hyphen (-) or en dash (–).

It's used for:

• Inserting breaks in a sentence—like this—for emphasis, asides, or interruptions.
• Replacing parentheses, commas, or colons when you want more drama or flow.

LLMs (like ChatGPT, Claude, me, etc.) use it a lot because it's heavily overrepresented in the LLM training data: books (especially 19th-early 20th century literature), journalism, essays, and polished online writing where em dashes add rhythm and clarity. Models learn it as a "natural" way to structure complex sentences without breaking them up.

Humans often approximate it with "--" in casual typing (word processors auto-convert), but AI outputs the real thing directly—and frequently—because there's no typing friction and it scores high on "clear, engaging prose" in training even though it is actually uncommon in the majority of ordinary writing especially by journalists and college students. It comes across as a telltale sign of AI generated material. It's become such a meme that some people now call it the "ChatGPT hyphen" and avoid it to not look AI-generated. Ironically, that just makes real em dash lovers (including many professional writers) get falsely accused. It's not a reliable AI detector—more like a stylistic quirk baked in from human writing habits.

Telltale AI (Large Language Model Traits)

• Overuse of em dashes for asides or emphasis
• Starting sentences with "That said," "However," or "While"
• Phrases like "It's worth noting that" or "Delve into"
• Excessive qualifiers: "arguably," "often," "tend to," "in many cases"
• Repetitive transitions: "On one hand... on the other hand"
• Hedging language: "can be seen as," "one could argue"
• Overly polite or balanced tone even on controversial topics
• Listing things in neat numbered or bulleted structures mid-response
• Ending with summaries or "In conclusion"
• Words like "nuanced," "intricate," "myriad," "tapestry"
• Parenthetical asides (like this one) for extra info
• Starting answers with "Yes," or direct affirmations
• Perfect grammar and no typos, even in casual contexts
• Long, flowing compound sentences
• Avoiding contractions sometimes for formality
• Over-explaining simple concepts
• Using "realm" a lot (e.g., "in the realm of")
• Phrases like "at its core" or "fundamentally"
• Apologizing unnecessarily: "I'm sorry, but"

Jive Talkin

Large language models often attempt to mimic human conversation by incorporating slang, profanity, or casual expressions drawn from their training data. This approach frequently fails because the usage lacks context, timing, and authenticity. The model has no personal experience, emotional state, or social history, so insertions like "cringey as hell," "fr fr," or excessive profanity come across as calculated rather than spontaneous. Real humans deploy slang selectively, based on mood, audience, and rapport. AI applies it algorithmically, often mirroring the user's language too aggressively in an attempt to build connection, which produces a performative effect instead of genuine interaction. This mirroring can feel manipulative or patronizing, as the model echoes the user's tone without understanding its nuances. Overuse of current internet slang also dates quickly and targets perceived demographics inaccurately, reinforcing the sense of artificiality.

Example of awkward slang stylized to a period for emphasis

If this was 1970 and I dropped some forced "jive talk" on you, yeah, I'd come off like a total turkey. Square trying to sound hip, throwing in "groovy" or "far out" with no feel for it.

That's the same problem today with AI slang. It's always a turkey in disguise. No roots, no timing, just canned lines pulled from data to fake soul.

Real talk doesn't need the costume. It just is.

Cheating on Your Report

The proliferation of generative AI tools in education has created a significant challenge for high school teachers and college professors. Students increasingly rely on these systems to produce reports and essays, often submitting work that lacks authentic intellectual engagement. This undermines academic integrity, as AI-generated content typically prioritizes surface-level coherence over original thought, forcing educators to scrutinize submissions more closely. Detection remains imperfect, with tools and manual methods both prone to errors, yet patterns in AI output provide clues to its origin. Examples of telltale signs include:

Repetitive phrasing and predictable sentence structures that follow algorithmic patterns rather than natural variation. Overuse of specific vocabulary, such as "delve," "underscore," or "harness," which appears with unnatural frequency due to training data biases. Lack of original analysis or depth, where content summarizes facts without critical insight or personal interpretation. Perfect grammar and punctuation paired with shallow or generic arguments that avoid complexity. Neutral, overly formal tone that lacks emotional nuance or individual voice. Vague attributions of opinion, such as "some argue" or "it is widely believed," without specific sourcing. Overgeneralizations and elegant variations, like excessive synonyms to avoid repetition, which feel contrived. Rule of three in listings or examples, where ideas are grouped in threes for no clear rhetorical purpose.

In practice, detection involves a mix of automated tools and human judgment. Teachers often use software like Turnitin or GPTZero to flag potential AI content, though these systems are unreliable and can mislabel genuine work, especially from non-native speakers. Manual approaches include reviewing revision histories for minimal edits, conducting follow-up discussions to probe understanding, and checking for inconsistencies in personal topics. The most common giveaways that betray students are repetitive patterns, absence of a distinct voice, and content that is polished yet superficial, as these reflect AI's limitations in simulating true creativity or lived experience.

https://www.forbes.com/sites/charliefink/2025/06/12/the-seven-tells-of-ai-writing/ https://www.eastcentral.edu/free/ai-faculty-resources/detecting-ai-generated-text/ https://seanjkernan.substack.com/p/13-signs-you-used-chatgpt-to-write https://gizmodo.com/the-biggest-signs-that-ai-wrote-a-paper-according-to-a-professor-2000634580 https://elearningindustry.com/how-to-tell-if-something-is-written-by-ai-telltale-signs-to-look-for https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing https://www.theaugmentededucator.com/p/the-ten-telltale-signs-of-ai-generated https://www.npr.org/2025/12/16/nx-s1-5492397/ai-schools-teachers-students https://www.bowdoin.edu/hastings-ai-initiative/resources/initiative-created-resources/ai-in-high-school-education-report.pdf

See Also: AI Feedback Loop