AI writing often feels like slop when it is relevant only in a broad sense, repeats itself, or keeps the same polished rhythm regardless of the audience. Researchers have measured some recurring differences in particular models and writing tasks, but “slop” is not a standardized scientific score—and no single phrase or grammatical habit proves that a passage was written by AI.
What people mean by “AI slop”
“Slop” is a reader’s judgment of generic or low-quality content, not a settled diagnostic category. In a 2025 paper, Chantal Shaib, Tuhin Chakrabarty, Diego Garcia-Olano, and Byron C. Wallace write that “There is currently no agreed upon definition of this term nor a means to measure its occurrence.” Their study develops a taxonomy based on interviews with experts in language processing, writing, and philosophy. The authors found that judgments can be subjective while still correlating with qualities such as coherence and relevance.
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That distinction matters: a passage can feel generic or unhelpful without revealing who wrote it. Human writing can be judged as slop, too, and AI-generated writing is not automatically perceived that way. Read the 2025 paper on measuring AI “slop”.
Why generated prose can sound repetitive or generic
Some models use a narrower range of styles
A 2025 study in the Proceedings of the National Academy of Sciences compared GPT-4o, GPT-4o Mini, and four Meta Llama 3 variants with human writing across academic work, news, fiction, spoken word, blogs, and TV and movie scripts. The researchers found substantial differences in grammatical, lexical, and stylistic features, and difficulty matching the variation associated with different genres.
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In the instruction-tuned models tested, present participial clauses appeared at two to five times the human rate, while nominalizations appeared at 1.5 to two times the human rate. Those results apply to the study’s models, prompts, and text collections—not to every current model or every AI-assisted passage. The authors interpret their findings as evidence that instruction tuning in the studied models was associated with more informationally dense, noun-heavy prose and less ability to match genre-specific variation.
As the authors put it, “We find large differences in grammatical, lexical, and stylistic features, demonstrating that LLMs prefer specific grammatical structures and struggle to match the stylistic variation present in human communication, particularly as that variation aligns with the conventions that structure genres such as academic writing, interactive speech, or journalistic news.” Read the PNAS study.
Uniformity can stand out more than any one phrase
A University College Cork account of James O’Sullivan’s 2025 stylometry work says that creative texts from GPT-3.5, GPT-4, and Llama 70B formed tighter stylistic clusters than human-written stories. The university describes the AI texts as polished and coherent, yet more uniform in word choice and rhythm. Across many pieces, that narrow range can help explain why generated writing seems to have a persistent house style.
It does not establish that a particular passage came from a model. O’Sullivan cautioned: “Stylometry can reveal broad patterns across large bodies of text, but it has no place in judging authorship in education.” Read the University College Cork account.
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Prompts, versions, and genre change the result
A 2026 article in The Modern Language Journal warns that even small prompt changes can affect linguistic features, and that findings may not carry over between model versions or configurations. It reviews evidence of a narrower, more repetitive repertoire of interpersonal expressions in some AI-generated writing and discusses limited rhetorical flexibility in relation to context.
Human writers also draw on subject expertise and communicative experience to make choices about stance and anticipate how readers will respond. This helps explain why a prompt that works for one task may fail for another; it does not mean that any one prompt reliably eliminates generic prose. Read the 2026 methodological article.
Repetition and “however” are editing problems, not authorship tests
Cambridge University Press & Assessment’s March 2024 account of research on AI-assisted essays identifies tautology, repetition, and overuse of “however” as reported hallmarks. Those observations do not make any of these features proof of AI authorship. Remove a repeated claim because it wastes the reader’s attention, not because it might fool a detector. Read Cambridge’s account.
How to edit an AI-assisted draft without flattening it
Use these checks as editorial judgment, not as a validated anti-slop algorithm. Keep changes tied to the reader’s needs rather than adding random variation.
- Check for repeated claims. If a sentence restates a point without adding evidence, a consequence, or a useful distinction, cut it or combine it with the earlier sentence.
- Replace generic relevance with specifics. Ask whether a sentence could fit almost any topic. If so, add a concrete fact, example, boundary, or consequence—or remove it. Relevance and coherence are among the qualities associated with slop judgments in the 2025 taxonomy.
- Vary rhythm where the meaning calls for it. Look for repeated openings, transitions, and sentence lengths. Reshape them to fit the information; do not insert arbitrary variation just to disguise a pattern.
- Turn abstractions into actions when that helps clarity. If a noun-heavy sentence buries who did what, name the actor and action. The PNAS study found elevated nominalization rates in its particular tested models, not a universal defect in every abstract noun.
- Match the genre and length to the task. A conversational explainer may not need report-like phrasing, a ceremonial introduction, or a conclusion that repeats its opening. Specify the reader and purpose, then remove material that does not serve them.
- Calibrate certainty to the evidence. Make clear what supports a claim and where its scope ends. A smooth style pass cannot substitute for checking the source.
- Keep editorial ownership in agent workflows. If an agent drafts, summarizes, or polishes, preserve links between claims and sources, then have a human editor assess relevance, support, and voice. This is an editorial safeguard, not a result established by a study of a particular multi-agent setup.
Can a writing tell prove that AI wrote a passage?
No. A high rate of a grammatical feature or a pattern across a collection can describe tendencies under specific study conditions; it cannot establish the authorship of an individual passage with certainty. A word, transition, punctuation mark, or polished tone is not proof. The cited studies examine particular models, prompts, genres, and corpora, and the prompt-sensitivity research cautions that results can change with model versions and configurations.
Use stylistic observations to decide what to edit, not to accuse a writer. The UCC account explicitly warns against using stylometry to judge student authorship, and the 2025 slop paper notes that human writing can also be perceived as slop.
What an agent workflow should optimize for
There is no evidence here that one agent architecture or prompt reliably prevents generic writing. Instead, evaluate the workflow on the actual kind of writing you need; outputs can shift with prompts and model versions. Compare approaches on these practical dimensions:
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- Audience and genre fit: Does the result suit the reader, purpose, and conventions of the format?
- Writer intent and voice: Does the draft preserve what the writer meant to say and the choices that make the piece theirs?
- Redundancy: Does editing remove repetition without erasing necessary distinctions?
- Human review burden: How much work remains to check facts, restore context, and make the prose useful?
A workflow that produces fluent sentences but leaves those questions unanswered has not solved the central problem: whether the writing serves its reader.
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