How to Use an AI LinkedIn Post Generator Without Sounding Generic

Start with an experience brief

Before generating, write five facts: what happened, what you expected, what changed, what evidence you observed, and what the reader can do differently. Include numbers only when you can explain where they came from.

For example, replace “write about better meetings” with “our weekly engineering meeting fell from 55 to 28 minutes after we moved status updates to an asynchronous template; explain the three changes and one downside.”

Choose a structure that matches the idea

A framework should make the idea easier to follow. It should not force every post into the same dramatic hook or one-sentence paragraph pattern.

• Lesson: situation, mistake, change, result, takeaway.
• Tutorial: outcome, prerequisites, ordered steps, common failure, next action.
• Contrarian view: common belief, disagreement, evidence, boundary conditions.
• Case note: problem, constraints, decision, measurable result, limitation.
• List: one clear promise followed by non-overlapping items.

Run a human edit in four passes

First, verify facts and remove invented specifics. Second, replace broad claims with the actual boundary of your experience. Third, read the draft aloud and rewrite phrases you would never use. Fourth, shorten the opening until the reader understands the subject before LinkedIn's see-more break.

Do not ask the model to make a post “viral.” Ask it to make the claim clearer, identify unsupported assertions, and suggest three openings with different levels of directness.

A practical final checklist

• The first two lines identify a specific problem or result.
• At least one detail demonstrates first-hand knowledge.
• Every number, quote, and product claim is verifiable.
• The post has one main idea rather than several weak ones.
• The ending invites a relevant response or gives a concrete next step.
• You would be comfortable defending the post in a client or team meeting.