The Interview-Driven Post: Forcing AI to Write Using Your Real-Life Stories

Last month I needed a blog post about why boutique brands should ban the word 'unfortunately' from customer service emails. I had the idea. I had the examples. What I didn't have was the patience to type it. So I interviewed myself for eleven minutes, ran the transcript through a prompt I've refined over two years, and had a draft that needed exactly seven minutes of editing. Published it the same afternoon.

Most people approach AI writing backward. They type a prompt like 'write a blog post about customer service tone' and get back a smooth, empty essay that could have been written by anyone. The interview-driven method flips the order: you generate the raw material first, then use AI to shape it. The result keeps your stories, your specifics, and your weird rhythms. The model just does the typing.

Why standard AI posts feel like they were written by no one

Ask an LLM to write a 1,000-word article on any topic and it will produce something structurally perfect and emotionally vacant. That's not a bug — it's the model's job. It has been trained to average out every opinion it has ever seen. What you get is the median take on your subject, delivered in the median voice, with examples that feel like they were pulled from a textbook.

The missing ingredient is specific memory. AI cannot access your 2019 client meeting where the founder cried because a shipment was late. It doesn't know that you test every prompt on your own bookkeeping spreadsheet before publishing it. It has never walked through your neighborhood coffee shop and noticed the milk-waste sign on the fridge. Those details are what make a post readable. Without them, you're publishing noise.

The interview layer — what it actually is

The interview layer is simple: you record yourself talking through the topic as if a curious friend asked you about it over coffee. Not a podcast. Not a script. Ten minutes of messy, digressive, specific monologue. You mention the tool you use, the client who taught you the lesson, the mistake you made last Tuesday. You swear once, probably.

Then you transcribe it — Otter.ai, Whisper, or even YouTube's auto-captions if you upload it as unlisted — and feed the transcript to the model with a single instruction: 'extract the article from this conversation.' The model isn't writing. It's panning for gold in a river you already filled.

Why voice beats notes every time

I've tested this against written outlines dozens of times. An outline gives the model structure but no texture. A voice memo gives both, because humans don't speak in bullet points. We tell stories, double back, emphasize with volume. The transcript preserves those rhythms as paragraph breaks and sentence lengths that the model can mirror.

The prompt that turns a transcript into a draft

This is the exact system prompt I prepend to every transcript. It has three jobs: preserve the stories, strip the filler, and enforce the structural rules I care about. Adjust the voice sample block to match your own writing.

You are an editor, not a writer. Your job is to extract a publishable {{FORMAT - e.g. blog post, newsletter, LinkedIn article}} from the raw transcript below.

TRANSCRIPT:
"""
{{PASTE_TRANSCRIPT}}
"""

VOICE SAMPLE (mirror this rhythm and specificity, not the topic):
"""
{{PASTE_TWO_PARAGRAPHS_OF_YOUR_BEST_WRITING}}
"""

EXTRACTION RULES:
1. Preserve every specific story, anecdote, and proper noun from the transcript. Do not generalize. "My client Sara" stays "Sara." "That $4,200 mistake" stays "$4,200."
2. Convert spoken digressions into clean narrative arcs. If I told a story out of order, reorder it logically.
3. Remove filler: "um," "you know," "I mean," "kind of," "sort of," and any sentence that starts with "So basically."
4. Replace spoken transitions with written ones. "And another thing" becomes a paragraph break. "Here's the kicker" becomes a bolded subheading.
5. Maintain my sentence-length variety. If I spoke in a short, punchy fragment followed by a long explanatory sentence, keep that rhythm.
6. Add no information that isn't in the transcript. If I was vague about a number, leave a [BRACKETED PLACEHOLDER] instead of inventing one.
7. Open with the strongest specific anecdote or claim from the transcript. Do not write a setup paragraph. Do not ease in.
8. Close with the last real point I made. No summary conclusion unless I actually said one.

BANNED SHAPES:
- No rhetorical questions in the opening
- No "in today's fast-paced world" or any variant
- No tricolons ("faster, smarter, better")
- No "ultimately" or "in conclusion"
- No paragraph longer than five sentences

OUTPUT:
The full extracted piece, then a short "Editor's Note" listing any placeholders I need to fill in and any sections where the transcript was too thin to extract.

How to tweak it for different formats

For a newsletter, change rule 7 to 'open with the most surprising single sentence from the transcript' and rule 8 to 'end with the concrete next step or link I mentioned.' For a LinkedIn post, compress the extraction to 250 words and let the model choose the strongest one-liner as the hook. For long-form essays, relax the paragraph cap to eight sentences and ask the model to preserve any extended analogies I used.

What the output looks like

Here's a real before-and-after. A transcript snippet from a recent recording about self-hosting automation tools:

After extraction, that became the opening of his guest post: 'Marcus runs a three-person design studio in Austin and pays $194 a month for eight Zapier workflows. When I told him he could run the same stack on a $5.50 Hetzner instance, he looked at me the way people look at flat-earthers. The difference is that n8n self-hosting is real, and we had him migrated by 6 PM.' Same story. Specifics intact. Filler gone. And the model didn't invent the $194 — it was in the transcript.

Model comparison for interview extraction

When this works and when it doesn't

The interview method shines when you already know what you think but you can't make your fingers type it. If you're exploring a topic you don't understand yet — genuinely trying to figure out your own opinion — skip the recording and do the thinking first. AI cannot interview you into a position you don't hold. It can only extract and shape the one you already have.

It also fails when the transcript is too thin. Seven minutes of generic advice with no stories, no numbers, and no proper nouns will produce seven minutes of generic prose. The extraction prompt doesn't create specificity. It protects it. If your recording is boring, the output will be boring too.

If you're looking for more ways to constrain AI output so it sounds like you, our guide on un-smoothing AI copy covers the structural side — banned shapes, rhythm rules, and forcing the ending first. Pair that with this transcript method and you have the full stack: raw material from your mouth, clean architecture from the prompt.