Turning AI drafts into publishable blog posts: a workflow

Publishing a raw AI draft is where content goes to be ignored. Here's the workflow we use to turn one into something worth reading, this post included.

Draft fast, but with a seed

Give the assistant more than a title. Feed it the audience, the angle, and one thing you personally know about the topic: a number from your own work, a mistake you made, a customer you talked to. A draft built around a real seed is far easier to make human than one generated from a bare prompt, because there's something true in it to build on.

Accept that the draft is a scaffold, not a post. Its job is to get the structure and the obvious points down so you can spend your effort on the parts that matter.

Humanize section by section

Paste the draft in sections and humanize each in Enhanced mode with Journalist readability: clear, varied, not academic. After each section, add one concrete detail of your own: an example, a screenshot, a genuine opinion. This is the step that turns generic competence into something with a point of view, and it's the step the model can't do for you.

Run the detector on the finished piece if you like; a lower estimated-AI score is a decent proxy for 'this has stopped sounding like everyone else'. But it's a proxy, not the goal.

The three edits that matter most

First, cut the introduction in half. AI intros restate the title and delay the point; readers and search engines both reward getting to it. Second, make every subheading a claim, not a label ('The three edits that matter most', not 'Editing'). Third, end when you're done, not with an 'In conclusion' that summarises what the reader just read.

Do those three things, add your specifics, humanize for rhythm, and you have a post that reads like a person wrote it because, in every way that matters, one did.