Can this prove text came from ChatGPT?

No. Refine estimates likelihood from writing patterns, not model fingerprints. A high score means the prose looks like common ChatGPT output, not that OpenAI generated it.

What ChatGPT patterns does Refine look for?

The scan checks for signals cataloged in our blog and humanizer skill: delve-class vocabulary, em-dash overuse, rule-of-three lists, negative parallelism, filler phrases, and repetitive cadence.

Will lightly edited ChatGPT text still flag?

Sometimes. Heavy editing can lower scores, but surface patterns often survive if you only swap a few words. Treat the result as an editing cue, not a verdict.

Can human writing look like ChatGPT?

Yes. Formal tone, list-heavy structure, and polished transitions can mimic ChatGPT house style. Always read the draft yourself before making authorship claims.

What should I do if my draft scores high?

Replace vague claims with specifics, break forced triplets, cut filler transitions, and read aloud for rhythm. You can also try Refine's humanizer to smooth robotic phrasing while keeping your meaning.

ChatGPT detector with pattern breakdown

ChatGPT detector

Check whether pasted text reads like ChatGPT output. Refine scans for house-style tells such as delve-class vocabulary, em-dash density, rule-of-three lists, and negative parallelism.

Free to tryChatGPT pattern signalsNo sign-in required

See how it works

Results are estimates only. Use them to guide editing, not to prove authorship.

How it works

Spot ChatGPT house style before you publish.

ChatGPT drafts often share the same surface habits. The scan highlights those patterns so you can edit for specificity and voice.

01
Paste ChatGPT output
Drop in a paragraph, essay section, email, or blog draft you suspect came from ChatGPT.
02
Run the ChatGPT scan
Refine estimates AI-likelihood from 0–100 and lists the ChatGPT-style patterns it finds.
03
Edit flagged patterns
Use the signal list to replace generic phrasing with specific claims, then review for accuracy.

ChatGPT-style signals

Patterns our blog and detector catalog for ChatGPT drafts.

These are editing clues, not proof. Learn more in our guides on AI vocabulary, em-dash overuse, and rule-of-three writing.

Delve-class vocabulary: Words like "delve," "landscape," "pivotal," and "underscore" show up far more often in ChatGPT edits than in everyday speech.
Rule-of-three lists: Ideas forced into neat triplets ("clarity, speed, and control") are a common ChatGPT rhetorical habit.
Em-dash density: Frequent em dashes used for drama or asides often stack up in ChatGPT first drafts.
Negative parallelism: Constructions like "not just X, but Y" create instant contrast that can feel staged in ChatGPT output.
Filler transitions: Phrases like "additionally," "it is important to note," and "in order to" pad sentences without adding detail.
Uniform cadence: Similar sentence length, repeated openers, and tidy transitions can make ChatGPT prose feel mechanically smooth.

Honest limitations

Pattern detection is not authorship proof.

ChatGPT is one of many models, and human writers can mimic the same structures. Refine cannot identify which tool produced a passage or whether AI was used at all.

Short samples and heavily edited drafts can produce misleading scores. Use this ChatGPT detector to spot stiff phrasing and guide revisions, not to make accusations or bypass review policies.

If your draft scores high, edit for specificity or try Refine's humanizer to smooth robotic phrasing while keeping your meaning intact.

FAQs