Straight answer: you cannot know for certain because the detectors disagree with each other constantly, but you can dramatically improve your odds by checking sentence rhythm, removing repeated phrase patterns, and running your draft through two or three different checkers before you publish. The bigger truth nobody wants to say out loud is that these tools are guessing, and treating their score as gospel will make your writing worse, not more human.
What happens when you run a detector
Last spring I took an 800-word section of a blog post I had written entirely myself, no AI involved, typed at my kitchen table over two coffees, and ran it through three detection tools out of curiosity after a client asked me to “guarantee” a piece would pass. Originality.ai gave it 12% AI. GPTZero gave it 61% AI. Copyleaks gave it 94% human. Same paragraph, same punctuation, same me. Three tools, three verdicts that did not agree with each other by even a rough margin.
That is the first thing you need to sit with before you go hunting for a checklist: these tools measure predictability, not authorship. They look at how likely each word is to follow the one before it, based on patterns learned from huge amounts of text, both human and machine written. Writing that is smooth, evenly paced, and grammatically tidy scores as “more AI” because that is literally what large language models are trained to produce. Writing that is a bit ragged, a bit uneven, with sentences that trail off or double back, scores as more human. Which means a first-year copywriter typing carefully and correctly can get flagged, while a bot told to “write casually, use contractions, vary sentence length” can slip through clean.
The uncomfortable part most people skip over
Here is the bit that gets glossed over in most guides on this topic: worrying about detector scores is often solving the wrong problem. Google has said publicly, more than once, that it does not use AI detection tools to rank or penalise content, and there is no verified case of a page losing rankings because a third party checker flagged it. The pressure to “pass” usually comes from a client, an editor, or a platform policy, not from the search engine itself. So you end up spending an hour reworking a paragraph to trick a tool whose own maker admits a false positive rate, rather than spending that hour making the writing sharper, clearer, or more useful to the person reading it. I have watched writers strip out a perfectly good, perfectly true statistic because a detector flagged the sentence structure, when the actual fix needed was nothing at all.
That said, if a client or platform requires a certain score, you do need a way to check before you send it, so here is how I do it.
My six-step pre-publish check
- Read it out loud first. If every sentence lands the same length and the same rhythm, that flatness is exactly what detectors flag as machine-written, and it is also just boring to read.
- Count your sentence lengths in one paragraph. Human writing naturally swings between short and long. A paragraph where every sentence is 14 to 18 words is a red flag both to a detector and to a real reader.
- Hunt for the “AI tells.” Words like “delve,” “unlock,” “,” “furthermore,” and the phrase “in today’s fast-paced world” show up disproportionately in generated text. If you spot three or more in a page, rewrite that section by hand.
- Run it through two checkers, never one. I use Originality.ai and GPTZero side by side. If they roughly agree, I trust the direction. If they wildly disagree, as they often do, I treat the score as noise and focus on the writing itself.
- Add something only you know. A specific number, a named client, a mistake you made, a date something happened. Detectors struggle with specificity because it is by definition unpredictable, and readers trust it more too.
- Check for repeated sentence openers. If three sentences in a row start with “This,” “It,” or “That,” a model has probably had a heavy hand in the draft, even if you edited on top of it.
None of that is a guarantee. But it moves you from guessing to checking, which is the most anyone can honestly promise on this.
Why AI drafts fail detection for a very specific reason
If you are drafting with tools like Claude, ChatGPT, or Jasper and then editing, it helps to understand exactly what you are fighting against. Models are trained to reduce what is called “perplexity,” meaning they pick the statistically most likely next word most of the time. That produces text that reads well but flows too smoothly, like a road with no potholes. Human writing has potholes. We repeat a word we already used two lines up because we did not notice. We write a six-word sentence right after a thirty-word one. We use “but” to start a sentence when grammar teachers told us not to.
If you use AI to get a first draft moving, which I still do for outlines and rough structure, the editing pass matters more than the prompt. I wrote about this in the best AI writing tools for bloggers, and the pattern holds across every tool I have tested: the output is only as human as the editing that follows it.
A short story about a client who learned this the hard way
A marketing manager I worked with last year had her agency deliver 40 blog posts over three months, all supposedly written by “senior copywriters.” She ran a batch through a detector after a colleague mentioned the tool existed, and 28 of the 40 came back flagged above 70% AI. She was furious, cancelled the retainer, and asked me to review the writing itself before she went further.
Here is what I found: the writing was fine. fine, on-topic, correct, reasonably useful. The problem was that every post followed the exact same five-paragraph shape, opened with a rhetorical question, and used the phrase “let’s break it down” at least once per article. It read like a template because it was a template, whether or not AI wrote a single word of it. That sameness is exactly what I mean when I talk about why so much business writing sounds the same now, and it is the real reason detectors flagged it, not because a robot typed it, but because a human had been told to write like one for the sake of consistency and speed.
The fix was not swapping words to dodge a checker. It was throwing out the template and letting each writer’s actual voice back into the piece, including the odd sentence that would never survive a style guide.
What I would not do, no matter what a client asks for
I get requests to “run this through a humaniser tool until it passes” more often than you would think, and I turn most of them down. Automated humaniser tools work by randomly swapping words for synonyms and inserting filler phrases to break up predictable patterns. They do lower detection scores. They also frequently introduce small factual drift, awkward phrasing, and sentences that technically parse but say something slightly different from what you meant. I have seen a client’s own case study numbers get subtly altered by one of these tools because it swapped “increased by 40%” for “grew by nearly half,” which is not the same claim and is not something you want sitting under your company name.
This is one of the tasks I would put firmly on the list of things not to hand over to automation entirely; I cover the wider version of that argument in which marketing tasks you should never automate, and detection dodging sits right at the edge of it. If a tool is rewriting your meaning to fool another tool, you have added a layer of risk for a score that three different checkers will disagree on anyway.
Where this matters for SEO
The one place I do tell clients to take detection seriously is when a platform or marketplace explicitly bans AI-assisted content and checks for it, or when a client contract specifies a maximum score. Outside of that, the SEO risk from AI-sounding writing is not the detector score itself, it is thin, repetitive, low-value content that fails to answer the question, which search engines can identify without any detector at all through engagement signals, duplicate patterns across the web, and simple relevance checks. I go through the difference between writing that reads fine to a checker but ranks badly, versus writing that might trip a detector but ranks well because it is specific and useful, in how to use AI for SEO the right way. The short version: chase usefulness first, detection score a distant second.
A quick reality check on the numbers
Turnitin, the tool most universities use, published its own accuracy data and states a false positive rate around 1%, but at scale across millions of student submissions, that 1% still means thousands of students wrongly flagged. GPTZero and Originality.ai do not publish comparably rigorous figures, and independent tests by researchers and journalists have found error rates ranging from single digits to over 20% depending on the text type, with technical writing, non-native English writing, and simple declarative writing style all more likely to be misflagged as AI generated. If you write short, clear sentences because that is good practice, you are statistically more likely to get a false flag than someone who writes in a meandering, clause-heavy style. That is worth knowing before you assume a high score means you did something wrong.
The checklist version, if you just want the short list
- Vary sentence length noticeably within every paragraph, not just across the page
- Cut any sentence that could have been written about literally any business in your industry
- Add one number, one name, or one specific date per section
- Read the draft aloud and mark anywhere you stumble, that stumble is usually the AI-sounding bit
- Check two detectors, not one, and treat disagreement between them as the real answer, not a problem to solve
- Stop editing for the machine the moment it starts making the writing less true or less useful
Frequently asked questions
Can a detector tell if I used ChatGPT to write a first draft and then heavily edited it?
Usually not reliably. Detectors score the final text in front of them, not its history. Heavy, genuine editing that changes sentence structure, adds specific examples, and breaks up rhythm typically drops the score significantly, because you have removed the statistical smoothness the tool is measuring, regardless of how the first draft came about.
Why did my 100% human-written content get flagged as AI?
Clear, correct, evenly paced writing looks statistically similar to what language models produce, since models are trained to be predictable and grammatically clean. Simple sentence structure, formal tone, and non-native English phrasing all increase false positive rates across every major detector currently in use.
Should I use a humaniser tool to lower my detection score?
I would not rely on one as a first choice. These tools swap words and insert filler to break pattern recognition, and in doing so they sometimes change the actual meaning of a sentence, particularly around numbers and claims. Manual editing for specificity and rhythm gets a safer result.
Does Google penalise content that scores high on AI detectors?
No confirmed cases exist of Google using third-party AI detection scores as a ranking factor, and Google has stated it evaluates content quality directly rather than relying on detection tools. Content fails to rank when it is thin or unhelpful, not because a checker flagged it.
Primary sources
Related reading: How AI Tools Can Improve Your Resume Writing (Without Making It Sound Like Everyone Else’s) and How a Core Content Strategy Improves Your Search Rankings (And Why Most Content Plans Don’t).
This builds on my main AI marketing guide, my main guide on the topic.
Related: identify remove fake instagram followers.
