Phrases that make a LinkedIn post read as AI-written

How to read this page

Every pattern below is a real rule in the Klype checker, taken from the code that runs on the free post checker. Each section says what the checker looks for, why a reader notices it and what a rewrite looks like. The examples are invented to show the pattern and are not taken from anyone's post. The reasons are our editorial judgement about how readers react, not research findings.

None of these patterns proves a person did not write the post. Plenty of people wrote "Let's dive in" before any software did. The point is that each pattern is generic, and generic is what readers skim past.

1. Stock openers

What the checker flags: openings such as "In today's fast-paced world" (also rapidly changing, digital, competitive, ever-changing), "In an era where", "Let's dive in", a first word like Unlock, Unleash, Elevate, Supercharge or Revolutionise, "The landscape is changing", "The answer is simple" and "Here's the truth".

Why readers notice: the line could open any post on any subject, so it tells the reader nothing about this one. They stop at the first line, which is the only line most feeds show.

2. Percentages with no source

What the checker flags: any percentage in the post when none of the phrases "according to", "source:", "study by", "report by", "survey by" or "data from" appears.

Why readers notice: a bare statistic is unverifiable, and one unverifiable number makes the rest harder to believe. Generated text is prone to produce confident figures that nobody measured.

The rewrite either names a source you have opened or drops the number. Do not invent a source to satisfy the rule; the checker only looks for the words, and readers look for the page.

3. Vague result claims

What the checker flags: "metrics were up", "engagement skyrocketed", "marked increase", "noticeable increase", "improved significantly", "the results speak for themselves", "transformative experience" and "game changer".

Why readers notice: an outcome with no measure behind it is a claim, not a result. Readers who have seen real numbers can tell the difference.

4. Invented stories

What the checker flags: two or more sentences that read as a scene told as fact. It matches an actor followed by an action verb ("a client asked", "we signed", "she realised"), an elapsed-time beat ("in seven minutes, we found"), "asked him", "told her", "showed them", and time anchors such as "last March", "a few years ago" or "I remember".

Why readers notice: invented anecdotes follow a recognisable shape: a named moment, a line of dialogue, a tidy turn. Anyone who knows the subject can tell when the detail that only a participant would know is missing. If readers catch one made-up story, they discount every real one after it.

5. A closing question to the reader

What the checker flags: a last line such as "Have you ever...", "What do you think?", "Do you agree?", "Thoughts?", "Share your experience", "Let me know in the comments", or any final line under 13 words that ends with a question mark.

Why readers notice: it is the same ending on thousands of posts, and it reads as a request for engagement rather than a thought that ended naturally. Our 500-post study found it on 28.2% of posts from one model, with the caveat that the number describes that one model only.

6. Summary and lesson endings

What the checker flags: a final paragraph that starts with "In short", "In summary", "In conclusion", "Ultimately", "At the end of the day" or "The bottom line", or text such as "my takeaway is". Separately, a last paragraph beginning "This shows", "It's a reminder" or a sentence like "Consistency is what matters".

Why readers notice: the post has already made its point. A summary repeats it, and a universal lesson could close any post at all.

7. Hashtag blocks

What the checker flags: three or more hashtags grouped on the last line.

Why readers notice: a trailing stack of tags looks like a template. In the 500-post study it appeared on 50% of posts, but only because that model added them to every plain and "engaging" post and to none of the others, so it describes that model's habit rather than AI writing in general.

8. Formatting tells

• Markdown asterisks. Text wrapped in double asterisks shows the asterisks literally on LinkedIn. It is a copy-and-paste leftover.
• Heavy em dash use. The checker flags more than one long dash (U+2014) per 100 words. Replace most with full stops or commas.
• "Not just X, it's Y". A phrase such as "it's not just about posting, it's about trust". Say the second half directly.

9. Rhythm and structure

• Three-item lists. The checker flags two or more runs of the form "a, b and c". Keep one at most.
• Enumerated lessons. Three or more sentences opening with First, Second, Third, Lastly or Finally.
• Flat rhythm. In a post of five or more sentences, sentence length varies by less than 3.5 words (standard deviation).
• Uniform paragraphs. Four or more paragraphs of about the same size.

People write unevenly. A four-word sentence, then a long one, then a fragment. Text that is level throughout looks assembled. The fix is mechanical: cut one sentence to a handful of words, let another run, and make one paragraph a single line.

What the checker does not do

It does not detect who wrote a post, and it does not guarantee reach. It scores the writing against these rules, starting from 100 and subtracting points per pattern, and it tells you what to change. Inside Klype, Check goes further: it compares the draft with your measured voice and with the facts you saved about your business.

Each rule also has its own page with the exact condition and more examples: the 15 writing patterns the checker flags.