How to Stop AI From Making Things Up in Your LinkedIn Posts

AI makes things up in your posts because it predicts what text is likely to come next, and it has no record of your life. To stop it, give it your real stories and numbers, tell it to use only those, have it leave blanks where it needs an example, and check every number, name and experience before you post.

Ask an AI for a LinkedIn post about hiring, and it may hand you a touching story about a junior developer you once took a chance on. It reads well. It also never happened.

This guide covers why that happens, how to cut it down with any AI tool, and what to check before you post. We make VoiceMoat, which has features for this, and we cover those near the end.

Why AI invents things in your posts

Language models generate text by predicting what's likely to come next. They don't look anything up in your life, because they have nothing to look up.

OpenAI's 2025 paper on hallucinations gives two reasons that matter here. Arbitrary personal facts, like a pet's birthday, can't be worked out from patterns. And the way models are trained and tested tends to reward a confident guess over admitting they don't know.

Your career is full of those arbitrary facts. And a typical LinkedIn prompt asks for exactly that kind of detail: a story, a result, a moment. So the model fills the space with something that has the right shape.

What AI tends to invent

These examples are invented, which is rather the point.

Why it's worth the trouble

The obvious risk is someone asking about it. A made-up client story falls apart at the first follow-up question, and on LinkedIn your employer, your customers and your old colleagues can all read it.

It's also your responsibility. LinkedIn's User Agreement says generated content might be inaccurate or misleading, tells you to "review and edit such content before sharing with others," and makes you responsible for it meeting LinkedIn's policies, including the rule against sharing misleading information.

How to stop AI making things up

1. Keep a list of what's true

Write down what's really yours: stories you tell often, numbers you can stand behind, opinions you hold. A plain note is enough. Ten honest items will do more than any clever prompt.

2. Give it that list and set a limit

Paste the list in and say it can only use what's there. Models are much better at working from material you provide than at remembering your life.

3. Have it leave blanks

If a post needs an example you haven't given, tell it to write [MY EXAMPLE HERE] instead of filling the gap. A visible hole is far safer than a smooth lie.

4. Ask it to show its sources

After the draft, ask for a list of every factual claim in it and where each one came from: your notes, or nowhere. It won't catch everything, but it often flags its own additions.

5. Read it for anything shaped like a fact

Look for names, numbers, dates, quotes, "last year" and "one of my clients." Those are where inventions sit.

6. Fix the source, not just the post

If it keeps adding the same kind of thing, change your instructions. Otherwise you'll be deleting the same sentence every week.

A prompt you can copy

Write a LinkedIn post about [topic].

Use only the facts below. Do not add any story, number, client, quote, result or experience that isn't listed. If the post needs an example I haven't given, write [MY EXAMPLE HERE].

My facts:

[paste your true stories, numbers and opinions]

After the post, list every factual claim you used and which of my facts it came from.

What a prompt can't fix

Instructions reduce invention, but they don't end it. A model can ignore a rule halfway through a long chat, and it forgets your list when you open a new one. Treat every AI draft as something a confident stranger wrote about you.

If you write this way every week, keeping your facts somewhere the tool can always see them starts to matter. Our guide to training AI on your own posts goes further on that, and how to spot an AI-written LinkedIn post shows what readers notice.

How VoiceMoat handles this

VoiceMoat is an AI social media tool for LinkedIn and Twitter. audenAI, the brain inside VoiceMoat, keeps your facts in one place and checks drafts against them.

The claim bank is the list from step 1, built from your own posts when you train your voice. It holds the claims you repeat, the stories you reuse and the framings you avoid. Each one comes with a word-for-word quote from your post and a link to it. Those quotes are checked in code against the original post, and anything that can't be found is dropped.

You stay in charge of it: Keep, Pin, Edit, or Not me, which removes an item and stops it being suggested again. You can add your own, and each platform has its own bank.

The check on drafts. Posts and threads you draft in Studio, and every post from Grow, are checked against your brief and your own material. A number, an event, an experience, a product claim or a result you didn't give is sent back to be rewritten, or taken out if it keeps coming back.

Learning from your edits. When you change a draft and then post, schedule or save it, audenAI learns what you changed. Once two edits agree, it suggests a plain rule you can keep or remove, and a rule you remove doesn't come back. You can pause this at any time.

You should still read every post. A check reduces mistakes, and it can't know that something you told it last year stopped being true.

What each plan includes

If you'd like your facts kept in one place, try VoiceMoat: Creator is $1 for 3 days.