The algorithm didn't flag your AI post. It flagged that nobody finished reading it.
MediaPost reports LinkedIn's own product chief, Hari Srinivasan, saying the platform is ramping up classifiers to catch AI slop, adding a 'seems like AI slop' flag button, and blocking billions of automation attempts. Founders read that and panic: should I stop using AI?
Wrong takeaway.
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The problem was never that a model wrote your post. The problem is that your post could have been written about anyone's product. A generic LLM has no memory of yours — no source links, no buyer-pain chain, no list of claims you never want marketing to make. So it writes fluent copy that invents a feature, rounds a number, or promises an outcome you can't back. Fluent and wrong is worse than rough and true.
The fix isn't 'humanizing' the output with fewer em-dashes. The fix is grounding it in facts the model actually knows.
Here's what that looks like in my workflow:
Before a single word gets drafted, I build an approved Product Context Document — a structured memory that ties the product claim, the buyer pain, and the proof point together. Features, verbatim pricing, proof, audience pains, differentiators, and the forbidden claims I never want on my feed. One document, approved once, reused everywhere.
Every post is generated from those approved facts. Then a post-generation hard filter runs before I ever see a draft — it drops any feature id not present in the PCD. On the marketing-filter benchmark that reaches 100% on-topic feature selection, 3.3× better than handing a raw LLM the full context, with zero invented feature references.
And every figure in a draft gets checked and traced to the sources I gave it. An untraced figure goes back once, then gets flagged for my review. No invented numbers on my feed.
This is the contrast that matters. Prompt a generic model daily and you get copy that reads like it belongs to anyone — the audience scrolls past, dwell time collapses, and LinkedIn quietly stops showing it. Ground the post in the product and it becomes specific enough that someone actually finishes it. Grounded beats fluent. Every time.
That's why I built Mindscast around this exact mechanism. The PCD builder extracts your features, pricing, proof, and forbidden claims from your own product sources — including a local path that runs inside your own Claude Code, Cursor, or Codex session, reading your repo locally and uploading only the structured PCD JSON you approve. Your source code never leaves your machine.
The product is the evidence for this stance, not the subject. The mechanism — approved facts in, filtered drafts out, every claim traced — is what makes a post worth finishing.
If your feed is full of fluent posts nobody reads, the answer isn't a better prompt. It's a better source of truth.
What does your post know that your product page doesn't?
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