Is my LinkedIn post AI-written?

The short answer

Nobody can prove it from the text alone, and that includes any detector. What you can do is check whether the post contains the patterns readers associate with machine-written text. If it does, it will read as AI-written to them, regardless of who wrote it. If it does not, it will usually read as written by a person with something specific to say.

A two-minute self-check

Read the post once, out loud if you can, and count how many of these are true.

1. The first line could open any post on any topic ("In today's fast-paced world", "Let's dive in", "Here's the truth").
2. It states a percentage and does not say where it came from.
3. It tells a short story with a time, a person and a line of dialogue, and you cannot add a detail only you would know.
4. It claims a result without saying what changed ("engagement skyrocketed", "the results speak for themselves").
5. Nearly every sentence is the same length, and every paragraph is about the same size.
6. It has "it's not just X, it's Y" or lists of three in several places.
7. It ends by asking the reader a question, summarising itself, or stating a lesson that could close any post.
8. It finishes with a block of hashtags, or shows literal asterisks around bold words.

One or two are normal. Four or more, and the post reads as generated. Each item above is a rule in the checker, described in full on the page of phrases that make a LinkedIn post read as AI-written.

Why a checker, not a detector

A detector claims to say whether a machine wrote the text. The Klype checker does not. It makes a narrower claim you can verify yourself: this draft contains these patterns, here is the sentence, here is what to change. You can disagree with a flag, and you can see exactly why it fired. It looks at the writing, so it works the same on a draft from ChatGPT, Gemini, a colleague or you.

Our own test shows why that matters. We had one AI model write 500 LinkedIn posts and ran them through the checker. The mean score was 79.9 out of 100, and 28.8% of posts had no flags at all. Asking the model to "make it sound human" lifted the mean from 62.4 to 94.2. The test used one model in one session, so it describes that model under those prompts and nothing wider. The full method, limits and dataset are in the 500-post study.

What to fix first

1. Unsourced figures. Name the source you have opened, or cut the number.
2. Invented scenes. Keep the story only if it happened, and add a detail only you would know. Otherwise state the point directly.
3. The first line and the last line. Replace stock openers with something concrete, and end on the last real thing you said, not a question or a moral.
4. Rhythm. Cut one sentence to a few words and let another run long.

When a flag is a false alarm

Rules are blunt. A few cases where a flag can be wrong, and what to do:

• A percentage you can source. If the figure comes from a study you have opened, say so in the sentence ("according to", "study by", "survey by"). The checker then stops flagging it, and so will readers.
• A real story. A scene that happened to you can trip the invented-story rule because the checker only sees the shape. Keep it, and make it unmistakably yours with a detail nobody else could supply.
• A short post. Under 40 words, rhythm checks are unreliable, and the checker says so.
• A question you mean. If the question is the actual point of the post, keep it. The rule targets the reflex ending, not every question.

What a free check cannot tell you

It cannot tell whether a figure is true, only that nothing in the post says where it came from. It cannot tell whether your story happened. It does not predict reach. Inside Klype, Check also compares the draft with the facts you saved about your business and with your measured writing voice, which is the part a general page cannot do.

More detail: signs a LinkedIn post is AI-written and the 15 writing patterns the checker flags.