How to humanize a LinkedIn post
Cut the setup and start at the specific thing. Generated LinkedIn posts open with a rhetorical question, build to an inspirational reveal, and end with a prompt for engagement. Delete all three, keep the incident that made you write, and say what you actually concluded.
Typical length · 100–300 words, where the first two lines decide whether anyone reads the rest. · Last reviewed 16 August 2026
Before and after
Before
What's the most valuable lesson you've learned in your career? For me, it's that failure is often the best teacher. Last year, our team faced a significant challenge. But through perseverance and collaboration, we turned it around. Sometimes the biggest setbacks lead to the biggest breakthroughs. What's your experience? Let me know in the comments!
After
We shipped a pricing change in March that lost us eleven customers in a fortnight. I had argued for it in three separate meetings. What I got wrong was not the price — it was that I never asked the four people who talk to customers daily what they thought would happen, because I had already decided and was collecting agreement.
The rhetorical question, the abstract lesson and the engagement prompt all went. What is left is a dated incident, a number, an admission of what the writer specifically got wrong, and no request for comments.
The tells here are structural, not statistical
This is the format where the discussion about AI detection is least relevant and the recognition is most immediate. Nobody scans a LinkedIn post. Readers identify generated ones in about a second and a half, and they do it from shape rather than from vocabulary.
The shape is a template: a rhetorical question to open, a short anecdote sketched at high abstraction, a lesson that could apply to anything, and a closing invitation to comment. Every element is designed to be broadly applicable, which is precisely why it reads as written by nobody about nothing.
The template also predates the tools. Consultants and engagement-farming accounts were producing it by hand for years, which is worth saying plainly: the problem is the formula, and a model reproducing it is copying humans who were already writing badly.
Start where the story actually starts
Almost every draft of this kind can lose its first two sentences with no loss of meaning. The rhetorical question and the throat-clearing are there to warm up; the reader does not need warming up, they need a reason to keep reading past the fold.
So open with the specific thing. A date, a number, a decision, an outcome. "We shipped a pricing change in March that lost us eleven customers in a fortnight" carries more than any question about lessons learned, because it is information rather than framing.
The same logic kills the closing prompt. "What's your experience? Let me know in the comments" is a request for engagement in place of a reason to engage. If the post says something arguable, people argue with it. If it does not, asking will not help.
Be wrong about something
The single most effective edit in this format is replacing the lesson with the mistake. Posts about perseverance and collaboration are unfalsifiable and therefore unreadable. A post about the specific thing you got wrong is a claim someone can disagree with, and it is the only kind that carries a voice.
It also solves the abstraction problem automatically. You cannot describe your own error at high abstraction — the detail is where the error lives, so writing it honestly forces you into the specifics the format is starved of.
There is a version of this that has itself become a formula, where the confessed mistake is flattering and resolves neatly. Readers spot that too. If the admission does not cost you anything, it is the same template wearing a different coat.
Formats with the same problem
Blog posts — the same formula given more room, and the same fix — start at the specific thing.
CVs and résumés — the private version of the same self-description problem.
Questions
- Do recruiters run posts through AI detectors?
- There is no reliable evidence of this being routine, and a short post falls below most detectors' stated minimums anyway. The realistic risk is a reader recognising the template, which requires no software at all.
- Is it wrong to use AI to draft a LinkedIn post?
- Not as a matter of rules — this is not academic work and there is no policy to breach. It is a question of whether the result is worth anyone's attention, and a post assembled from a template usually is not, whoever assembled it.
- Why do em dashes get blamed for this?
- Because they turn up often in generated prose and are easy to spot, which made them a convenient signal. They are also correct punctuation that plenty of people use deliberately, so removing yours to avoid suspicion means writing worse to satisfy a rumour.
- How long should a post be?
- Short enough that the specific thing appears before the fold. Most of what makes these posts long is setup, and cutting the setup is usually the whole edit.
What to watch for
- Naming an employer, a client or a colleague in public has consequences a rewrite will not warn you about.
- Real numbers are what makes this format work, so check them before posting rather than after.
- Nothing here is about detection. Nobody is running LinkedIn posts through a detector; they are just recognising the shape.
If your writing gets flagged
Rewriting for rhythm and specificity tends to lower detection scores, because that is what detectors read as human. It is not a guarantee — detectors disagree with each other and change without notice, and we do not promise a result from any of them.