LLM Fingerprints in Text

Dec 7, 2025

We are being fed more LLM generated text than ever before. Often, this is not disclosed, so it has become a useful skill to spot the subtle (and somtimes not so subtle) fingerprints they leave behind. Once you learn to detect them, you cannot unsee them. They appear in marketing, social media posts, news, basically everywhere.

These fingerprints are often the result of the model trying too hard to be helpful, profound, or engaging. Here are the most common tells that suggest a human didn't write what you are reading.

"Not THING; It's METAPHOR"

LLMs have a tendency to use a specific rhetorical structure to sound insightful. It follows the pattern: "This isn't just [simple thing]; it's [profound metaphor]." It attempts to elevate a mundane subject into something grand.

"That's not just investing; that's the gravitational pull of capital."

"Coding isn't just typing; it's a symphony of logic."

This structure is effective in moderation. But when every paragraph tries to redefine reality with a semicolon, it becomes exhausting. It feels like the text is constantly trying to sell you on the importance of its own existence.

Em Dashes Everywhere

"The future of technology—driven by AI and automation—is reshaping our world."

LLMs love the em dash. They use it to insert explanations, add emphasis, or simply because they seem to prefer it over parentheses or commas. And while the em dash is certainly a useful tool in writing, LLMs tend to overuse them so much that every second sentence contains a dramatic aside.

The "In Conclusion" Ending

High school English teachers taught us to summarize our points at the end of an essay. LLMs took this advice to heart and never let it go. Almost every generated response ends with a paragraph starting with "In conclusion," "In summary," or "Ultimately."

This final paragraph often restates what was just said without adding new value, usually wrapping up with a moralizing or optimistic platitude about the future.

Structural Repetition

Humans vary their sentence length and paragraph structure to keep the reader engaged. LLMs, however, often fall into a rigid rhythm. You might notice a scrolling pattern that looks like a form being filled out: a heading, followed by exactly two sentences of introduction, a list of bullet points, and a single concluding sentence.

Then, the next section does exactly the same thing. This "cookie-cutter" formatting tends to occur, since LLMs are always prediction the next most likely token, leading to repeating patterns, as soon as a pattern is established.

Perfect Grammar, Zero Substance

LLM text is grammatically flawless but often lacks depth. It can write three paragraphs that sound professional but say absolutely nothing concrete, like this:

"To improve efficiency, one must utilize efficient methodologies that streamline processes for maximum optimization."

It reads smoothly, but there is zero information density. Reading stuff like this feels like eating cold soggy fries.

The "Both Sides" Safety Net

Trained to be harmless and neutral, models often refuse to take a hard stance. Even when asked for a critique, the text will often hedge heavily with phrases like "It is important to approach this with balance" or "While X has its downsides, Y offers benefits."

This results in a diffuse tone where the text constantly counter-signals its own points to avoid offending anyone, stripping the writing of any real conviction or personality.

The Emoji Overload 🚀✨

When asked to write a social media post or a "engaging" article, models often sprinkle emojis at the end of every sentence or paragraph. Especially when code documentation or technical topics are involved, the spamming of emojis is almost guaranteed. It feels tiring, like a corporate brand trying too hard to relate to "the youth."

Hallucinations

LLMs don't know what they don't know. They are statistical parrots, so they will confidently generate false information, made-up quotes, and non-existent studies. Especially if some factual data is "surprising" or unlikely, it will often be "corrected" by the model into something more probable but false.

In Conclusion

Recognizing these patterns helps us filter the noise, helping us to identify real and qualitative human writing from AI-generated "fast food".

In conclusion, delving into this rich tapestry of linguistic quirks isn't just an analysis; it's a testament to our evolving digital landscape—fostering a new realm of understanding—where we can bridge the gap between human and machine 🚀✨.