How to Make AI Social Media Posts Not Sound Like AI

I build an AI product that writes social posts for small businesses, which means I spend a weird amount of my life studying why AI writing smells like AI. The good news: the smell has causes, and the causes have fixes. These are the rules we actually enforce in code, plus the one failure we caught that changed how I think about all of it.

Why do AI posts sound like AI?#

AI posts sound like AI because they are fluent about nothing. The model produces grammatically perfect, rhythmically even sentences built from abstractions, because without real input facts, abstractions are all it has.

Readers are primed to notice. A 2026 Klaviyo study found 87% of consumers believe they can tell when a company is using AI, and Skyword's survey put the cost plainly: about a third of consumers trust a brand less when its content is AI-generated. Whether they detect it accurately matters less than the fact that "sounds generated" now reads as "does not care." For a small business whose whole advantage is being real and local, that is the worst possible trade.

What actually makes writing read as human?#

Specificity. A post that says "the island took three weeks because the stone came in wrong" reads human. A post that says "quality craftsmanship you can trust" reads generated, even if a person wrote it.

This inverts how most people use AI. They spend effort on the prompt ("write an engaging post for a kitchen remodeler") and no effort on the input. We do the opposite: posthell's writing engine is not allowed to write until it has facts from your actual week, and the system prompt forbids it from using anything else. Three plain sentences of what happened beat any amount of prompt engineering, because the model can only be specific with material it was given. If you have never articulated what your voice even is, do that first; the voice guide is the short version.

What is the failure mode to watch for?#

Invented specifics. Ask an AI to sound concrete without giving it concrete facts and it will manufacture some, confidently, in your name.

Here is one we caught live. During testing, a user told the model they post "two videos a week." The draft came back promising "new videos every Tuesday and Friday." No one ever said Tuesday or Friday. The model needed specificity, had none, and fabricated a schedule, which on a business page is a commitment to customers. That single test produced our hardest rule: grounding only. The model may compress, rephrase, and restructure what you said. It may never add a fact you did not say. When we later added a mode for general topic posts, we kept a separate rule banning fabricated first-person claims and fake statistics, because the moment "I" appears in a post, every claim after it must be true.

Which surface-level tells should you ban outright?#

Ban the em-dash, the buzzword list, and the formula closers explicitly. Models will use all three forever unless told not to, and each is now a recognized signature.

Our enforced list, which you can copy into any prompt:

Tell	Example	Replace with
Em-dashes	The long dash models put in every third sentence	Commas or a period
Benefit abstractions	unleash, game-changer, revolutionize, "hidden gem"	The concrete thing that happened
Manufactured contrast	"It's not just a bakery, it's an experience"	Say the actual thing once
Platitude closers	"Exciting things ahead!"	End on the fact or a real question
Uniform rhythm	Three balanced sentences in a row	One short sentence. Then a longer one with a number in it.

Two implementation notes from doing this in production. First, banning words in the prompt is roughly 90% effective, so we also strip em-dashes in code after generation, deterministically. Second, the bans must be specific: "avoid sounding like AI" does nothing, while "never use the word unleash" works.

Why do you still need an editor pass and an approval step?#

Because generation rules catch known tells, an editor pass catches new ones, and only a human catches "technically fine but I would never say that."

We recently moved to a two-stage pipeline where every generated post goes through a slop check and, when it trips, a rewrite pass by a second model acting purely as an editor. It catches formula the first pass produced despite the rules, and it noticeably reduced the near-misses. But the last line is unchanged: nothing publishes until the owner taps approve. That is not a legal disclaimer, it is the quality system. You will spot in two seconds what no automated check can, because only you know what you actually sound like and what actually happened. The broader safety argument is in is it safe to let an AI agent post for you, and the full picture of what an AI manager should do around the writing is in the AI social media manager guide.

Where to start#

Take the last AI-written post you almost published and run the two tests. The grounding test: is every fact in it something you actually told the tool? The face test: would you say this sentence to a customer standing in front of you? Fix the post until both pass, and notice which edits you made. Those edits are your personal ban list, and they will do more for your feed than any new tool. If speed is the reason you reached for AI at all, writing posts faster covers the workflow half.