How to write blog posts with AI (that do not read like AI)

The mistake is drafting first

Almost everyone starts by asking for a draft. That is why almost every AI blog post reads the same: the model has nothing to work from except the average of everything written on the topic, so it returns exactly that. Fluent, structurally correct, and indistinguishable from the twelve posts already ranking.

Writing a blog post is three jobs, and only the third one is drafting. First you find out what has already been said. Then you decide what you are adding. Only then does anyone write a sentence. Each job also has a different best model, which is what makes a single-model workflow feel like it is fighting you at one stage or another.

Stage	Model	What it contributes
Research and existing coverage	Gemini	Recent web material, with sources you can open
Angle and thesis	Any, used adversarially	The gap in existing coverage, and the objection to your take
Structure and outline	GPT	Holds a brief, produces clean hierarchy, no drift
Prose	Claude	Warmest register, least filler, sustains a voice
Fact check	Gemini or a web-connected model	Retrieval against live sources, not memory
Final edit	You	The part that is not automatable and never was

Because Whizi keeps the thread when you switch models, the drafting model sees the research and the outline. That continuity is most of why the finished post is coherent rather than a set of well written paragraphs that do not add up.

Step 1 and 2: research, then find the gap

Prompt: what already exists

Research current coverage of [topic]. Return: the top pieces currently ranking with their URLs and publication dates, the argument each one makes, the points every single one of them makes (the consensus), any recent data or developments from the last 12 months with sources, and the questions readers ask that these pieces do not answer. Cite everything. Where you cannot verify a claim, mark it UNVERIFIED.

Prompt: the gap

Based on that coverage, what is missing? Specifically: what does every piece assume without arguing for it, what would an experienced practitioner find naive about the consensus, what question do they all avoid because it is hard, and what would be true for a reader for whom this standard advice fails?

That second prompt is where a post stops being interchangeable. The output is not your angle, it is a list of candidates, and the one you pick has to be something you actually know to be true. A model can find the gap; it cannot fill it with experience it does not have.

Prompt: pressure test your angle

My thesis is: [state it in one sentence]. Argue against it as forcefully as you can. What is the strongest counterexample, what would I need to demonstrate for this to hold, and is there a narrower version that is more defensible? Do not agree with me.

Step 3 and 4: outline, then draft

Prompt: the outline

Build an outline for a post arguing [thesis]. Audience: [who they are and what they already know]. Return H2s and, under each, the specific point it makes and the evidence it uses. Rules: every section must advance the argument rather than covering a subtopic, no "what is X" section unless the audience genuinely does not know, no conclusion that only restates. Flag any section where I have not given you evidence. Research: [reference the thread above].

The evidence gap flag is what stops you writing 800 words before discovering you cannot support the central claim.

Prompt: draft a section, not the post

Write the [section name] section. Point it makes: [from the outline]. Evidence: [paste]. Voice: [paste 3 paragraphs of your own writing]. Rules: open with the point rather than context, no sentence that would appear unchanged in a post by a competitor, every claim traceable to the evidence given, no adjective that cannot be measured, no transitional filler. Length: roughly [n] words.

Draft section by section rather than asking for the whole post. Full-post generation drifts toward summary, whereas a section with a defined job and its own evidence stays specific. It also means you catch a bad section at 200 words instead of at 1500.

Prompt: criticism before rewriting

Before revising, name the three weakest things in this draft: a claim with no support, a paragraph that says nothing new, a sentence that could open any post on this topic, or a place where the argument skips a step. Quote each. Do not rewrite yet.

The tells, and how to remove them

Readers recognise AI writing quickly, and it is a short list of specific habits rather than a vague quality. Each one has a fix.

The tell	What it looks like	The fix
The throat clear	"In today's fast-paced digital landscape"	Instruct: open with the point, no scene setting
Symmetrical everything	Every section the same length, every list exactly three items	Ask for uneven emphasis: some points deserve a sentence, some a page
Unearned balance	"While X has benefits, it also has drawbacks" applied to everything	Require a position, and require the strongest objection to be answered rather than noted
Benefits without mechanism	"Improves efficiency"	Require every claim to name how it works
The restating conclusion	A final section that summarises what you just read	Ask for a conclusion that tells the reader what to do or what changes
Nobody's voice	Correct, smooth, could be anyone	Paste real samples of your writing rather than describing your tone

The single highest-impact fix is the last one. Three paragraphs of your own real writing pasted into the prompt does more than any list of tone adjectives, because the model can imitate an example and cannot imitate a description.

Then add what a model cannot: the specific number from your own work, the thing you tried that failed, the objection your customer actually raised. One concrete detail from real experience does more for credibility than an entire pass of stylistic editing.

Step 5: the fact check you do not get to skip

A fabricated statistic on your blog is permanent, quotable, and yours. Models produce plausible numbers attached to plausible sources with total confidence, and the failure is invisible in the output.

Prompt: audit the claims

List every factual claim, statistic, date, and attributed quote in this draft as a table: the claim, whether it came from a source in this conversation or from your general knowledge, and the source URL if there is one. Mark everything from general knowledge as UNVERIFIED. Do not fill gaps with plausible values.

Then open every source yourself. Not the ones you doubt, all of them. The most common error is not an invented statistic, it is a real statistic attached to the wrong claim, or a 2019 figure presented as current, and neither is visible without opening the page.

Two things to check specifically: that the number still refers to what you say it refers to, and that the source is the original rather than someone else citing it. Statistics degrade as they get passed along, and a figure three citations deep is often unrecognisable at the origin.

What this realistically saves

A 1500 word post that took three hours takes about 45 minutes with this workflow. The saving is concentrated in research, outlining, and getting to a first draft, which were always the slow parts.

What it does not compress is the final edit, and you should not try. Expect to keep perhaps 80 percent of the generated prose and rewrite the rest, particularly the opening, the transitions between arguments, and anything drawing on your own experience. That last pass is where the post stops being competent and starts being worth reading.

On search: Google's guidance targets low quality content, not AI assistance as such. A post that is accurate, genuinely useful, and adds something the existing coverage does not will do fine. A post generated in one prompt and published unedited will not, and increasingly it will not even be indexed properly, because there are already several thousand of it.