AI slop is content an AI model produces without original research, named sources, or a point of view, so it gives readers and answer engines nothing unique or useful. The term spread so fast that Merriam-Webster named “slop” its 2025 word of the year, crediting generative artificial intelligence for much of its rise.

Our free AI slop detector scores a draft for those gaps. It runs the same checks I apply to AI-generated content before it ships to a client. Paste a draft to get a score out of 100, a breakdown across five categories, and the exact lines that cost you points.

Note: This tool won’t guess who wrote it; that’s something a standard AI detector tool answers.

What the AI Slop Score Measures

Data uniqueness carries the most weight because it’s the one category a language model can’t fill on its own:

Every check counts patterns per 100 words, so a long draft isn’t punished for its length. Each pattern has a cap (for instance, one stray dash can’t sink a score). A score of 70 or higher means low slop risk, 40 to 69 is mixed, and anything under 40 is high risk. It checks for 23 AI red flags in the Fractl Style Guide. It also checks the voice and sourcing rules a pattern can catch, and each flag names its rule, such as AI-9 for a negative flip (“it’s not X, it’s Y”). Rules that take judgment, like whether a sentence states a fact or only hints at one, still need an editor.

Before and After: How We’d Rewrite a Slop-Flagged Passage

We had an AI model write a paragraph on how digital PR earns AI citations, with no data or sources to work from. It scored 15 out of 100:

Our rewrite keeps the topic and replaces every generic claim with something from Fractl’s own work:

Data uniqueness and opinion strength each went from zero to full marks once the draft had figures only Fractl has and a trade-off it was willing to defend:

A perfect score means the rewrite cleared every pattern check. It doesn’t mean an answer engine will cite it, and it doesn’t replace an editor reading the paragraph before shipping or publishing it.

AI Slop Detectors vs. AI Content Detectors

AI checker, AI text detector, AI writing detector, and ChatGPT detector are all names for the same kind of tool: an AI detection model that estimates whether text came from ChatGPT, Claude, Gemini, Copilot, or one of the other chatbots and virtual assistants built on large language models (LLMs). GPTZero, ZeroGPT, Copyleaks, QuillBot, and Grammarly all offer one. Most return an AI score: a probability that the text is AI-written content.

Under the hood, AI content detection is a natural language processing (NLP) task handled by machine learning classifiers. OpenAI built its own as a language model fine-tuned on pairs of human and AI writing on the same topic. Sapling says it regularly retrains its detector to keep up with new models such as GPT-5, Claude 4.5, Gemini 2.5, Qwen3, and DeepSeek-V3. Its free version checks up to 2,000 characters at a time, and Sapling also sells an API and a browser extension.

Pangram says its detector doesn’t need retraining for each new release, and it caught GPT-6 Astra output in 982 of 986 test samples. Pangram also flags text for which an AI model was only used for editing, labeling those sections lightly or moderately AI-assisted.

A plagiarism checker is a third, older tool. Plagiarism detection compares your text against pages that already exist, while an AI detector looks at the text itself. Neither one tells you whether a page is worth reading:

Plagiarism Checkers vs. AI Content Detectors vs. AI Slop Detectors
Question	Plagiarism checker	AI content detector	AI slop detector
What it answers	Was this copied from an existing page?	Did a model write this?	Is this worth reading and citing?
How it decides	Matches your text against published pages	Runs a classifier trained on human and AI writing	Counts specific writing patterns per 100 words
What you get	A match percentage with sources	An AI score or probability	A 100-point score with flagged lines
Best for	Catching copied passages	Academic integrity reviews	Editing a draft before it ships

How Accurate Are AI Detection Tools?

Accuracy in AI content detection depends on the tool, and the early tools were weak.

OpenAI built its classifier in part to help detect AI-generated content in cases like academic dishonesty and automated misinformation campaigns. It caught 26% of AI-generated text and labeled human writing as AI 9% of the time. OpenAI pulled it on July 20, 2023, because of its low accuracy. OpenAI also warned that the classifier would sometimes label human writing as AI with high confidence.

Commercial tools have improved, though. In a 2025 Chicago Booth study, all three commercial detectors kept false positive rates below 1%, but detection accuracy shifted with the length of the text, the model that wrote it, and the detection threshold a user set. The false positive rate is the share of human writing a detector wrongly flags as AI, and the false negative rate is the share of AI-generated text it misses. In academic integrity cases, even 1% means one in 100 honest students could face a false accusation.

For a marketing team, the bigger question is what a flag means. Fractl drafts much of our content using Fractl Agents, so an AI content detector can correctly flag our drafts. Google doesn’t penalize AI use on its own, but it warns that using generative AI to publish many pages without adding value may count as scaled content abuse under its spam policies.

The useful test for a draft is whether it adds anything a reader can’t find elsewhere. That’s what this slop detector scores on, and one of the many human-led checks all our content passes before it gets published.

What AI Slop Costs You in AI Search

The research on what earns citations from LLMs and AI Overviews (AIOs) points in different directions, so we’d treat any tool that promises citations with suspicion:

Surfer’s explanation for its result is cost: a passage near the top of a page takes less computing power to retrieve, though Surfer calls that a reasonable model rather than a proven cause.

Together, the studies point to ranking as the main way into AI answers, with a page’s opening paragraph as its likeliest part to be quoted. That makes slop a ranking problem and a barrier to AIOs, because readers who don’t trust a page will leave it, and publishers also won’t link to it.

In our survey of 2,000 Americans with Search Engine Land, distrust of AI-heavy marketing doubled from 20% to 40% in a single year. Pages built on commodity content that any model could write give those readers a reason to leave and give publishers no reason to cite you.

How Fractl Keeps AI-Assisted Content Out of the Slop Pile

We use generative AI every day. Fractl Agents, our suite of AI agents, runs production Plays from pitch strategy to survey writing and content development, and each Play draws on research files that aren’t in the base AI models’ training data.

I add an editorial layer on top of that. When I edit an agent’s first draft and it lacks proper citation density, I send it back for sources before I make even one manual change. That’s how we use AI for speed without publishing pages a reader could just get from ChatGPT. It also means our SEO and GEO work begins with pages that have something new to rank, including original data and named sources (not to mention UX that keeps readers on the page and helps with ranking, too).

Score Your Next Draft Before It Ships

Run your next draft through the detector before it goes to an editor, and start with the two categories at the top of your results. If you want a partner that builds content around data no model can reproduce, reach out and partner with Fractl.

Frequently Asked Questions