How to Use AI to Write Blog Posts That Don’t Read Like AI

There are four things that make writing detectable as machine-generated, and they’re all structural. Not word choice — everyone already knows about “delve” and “leverage.” The tells that actually give it away are about shape.

Fix the shape and the vocabulary problem mostly disappears on its own.

Tell 1: every section is the same length

Open any AI draft and measure the sections. They’ll be within ten percent of each other. That’s because the model allocates effort evenly — it has no reason to think one point deserves four hundred words and another deserves forty.

Human writers don’t do this. They spend three paragraphs on the thing they care about and one sentence on the thing they’re obligated to mention. The imbalance is the signal.

The fix: decide the section lengths before you generate anything, and make them deliberately uneven.

Tell 2: nothing is specific enough to be wrong

“Many businesses struggle with content consistency.” True of every business, therefore useless, therefore unmistakably generated.

Specificity is the hardest thing to fake because it requires having been somewhere. “Three of my last five clients had a content calendar they’d stopped using by week four” can only be written by someone who was there.

The fix: write the fact list yourself, before any generation. Not research — your own material.

The [NEED:] instruction is doing the heavy lifting. It converts a hidden gap into a visible one, which is the difference between a draft you can fix and a draft you have to fact-check.

Tell 3: every claim is hedged

AI drafts are perpetually balanced. Every assertion comes with a qualification. “While X is often effective, it’s important to consider that Y may vary depending on context.”

This is trained behaviour — models are optimised to avoid being wrong, so they refuse to be definite. It produces writing that cannot be disagreed with, which means it cannot be remembered either.

The fix: force a position, and make the position cost something.

Then read it and ask: would anyone disagree? If not, the position is too safe to be worth publishing.

Tell 4: transitions that only exist to connect

“That said.” “Moreover.” “In addition.” “It’s worth noting that.” These exist because the model is constructing paragraphs sequentially and needs glue.

Human writers use far fewer, because they think in claims rather than sentences, and claims don’t need glue — they need proximity.

The fix: delete every transition and read it again. If the piece still makes sense, the transitions were noise. It usually does.

The workflow that works

Not “ask AI to write a post, then edit it.” That produces an edited AI post, which still reads like one.

This order matters:

1. You write the title and the position. Two sentences. No AI. If you can’t state the position in one sentence, you don’t have an article, you have a topic.
2. You write the fact list. Ten to fifteen bullets of things only you know. This is the twenty minutes that determines whether the piece is any good.
3. You specify the section lengths. Deliberately uneven. Section 2 gets most of it.
4. AI expands each section separately. Not the whole article in one go — one section per prompt, with the fact list attached each time. Whole-article generation is where the symmetry creeps back in.
5. You rewrite the opening and the ending yourself. These are the two parts readers judge, and they’re the two parts where AI’s averaging is most visible.
6. Cut 15%. Run the draft through a cutting pass, then cut again by hand. The second cut removes things the model thought were load-bearing.

Time: about ninety minutes for a 1,500-word piece, of which twenty are you and AI, and seventy are you.

That ratio is the point. AI compresses the middle — the expansion, the structuring, the cutting. It cannot compress the beginning, because the beginning is the position, and positions come from having been somewhere.

What not to use it for

Anything where the value is the specificity. Case studies, customer stories, “what happened when I tried this.” The moment the details are invented, the piece is worthless, and readers can tell even if they can’t say why.

Anything where being wrong is expensive. Technical claims, legal or medical statements, numbers you haven’t verified. The model will produce confident, plausible, unverified statements, and they’ll be in your voice.

The first draft of your thinking. This is the one people get wrong. Using AI to figure out what you think produces a well-organised version of the conventional view. Think first, on paper, badly. Then use AI to structure it.

The test before you publish

Read the piece out loud. Three things to listen for:

• Does any sentence surprise you? If not, the piece contains nothing you didn’t already know, which means it contains nothing the reader doesn’t already know.
• Could a competitor publish this unchanged? If yes, it’s category content, not your content.
• Is there one sentence you’d be slightly nervous to publish? That sentence is usually the only reason to publish the piece.

Three questions, ninety seconds. They catch more than any AI detector will.

The uncomfortable part

The people whose AI-assisted writing doesn’t read as AI-assisted are, without exception, people who could write the piece themselves.

They use AI to go faster, not to skip the part where they have to know something. The tool amplifies a point of view that already exists. It doesn’t create one.

Which means the skill worth developing isn’t prompt engineering. It’s having opinions specific enough that no model could have produced them from the average of the internet. That’s the whole moat, and it’s the one thing that doesn’t get cheaper as generation does.

If you want the prompts

The four prompts above — the structure spec, the fact-list constraint, the position forcing, and the cutting pass — are the core of what’s in the Content Engine, alongside the theme ladder and the 60 hooks.

But start with step 2. Write fifteen facts only you know about your subject, tonight, before you open any tool. Everything else in this article is downstream of that list.

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