What this prompt does
This prompt expands a short outline into a publish-ready 1500-word technical article for developers. It takes three context variables — [audience], [language], and the [outline] — and enforces a specific shape: a hook first sentence with a concrete number or sharp claim, problem framing in the first paragraph, two to three code examples in the chosen language with explanation, a comparison table where useful, a short three-question FAQ, and a conclusion with a next-step call to action. The tone is fixed as direct, opinionated, and first-person, with explicit instructions to avoid AI clichés.
The structure works because it front-loads the two things that decide whether a technical post ranks or gets skimmed: a sharp hook and real, runnable code. Setting [audience] (for example, senior backend engineers) calibrates the depth so the piece doesn't over-explain basics or skip necessary context. Pinning [language] keeps the code examples consistent and idiomatic. The first-person, no-clichés constraint is what keeps the output from reading like generic AI filler that search engines and readers both discount.
When to use it
- Turning a rough outline into a finished developer-facing article
- When you write in a first-person, opinionated voice and want it preserved
- When a post needs real code examples, not hand-wavy snippets
- When you want a consistent structure (hook, framing, code, table, FAQ, conclusion)
- When you're producing technical content at a steady cadence and need a reliable scaffold
- When you want to avoid the AI-cliché phrasing that flattens a piece
Example output
You get a roughly 1500-word Markdown article: a hook sentence leading with a number or sharp claim, a first paragraph that frames the problem, two or three explained code examples in [language], a comparison table where it adds value, a three-question FAQ, and a conclusion ending in a next-step CTA. The voice is direct and first-person throughout. It's meant to be edited and published rather than treated as a final draft you ship untouched.
Pro tips
- Make
[outline]as detailed as you can; the richer the outline, the less the model invents and the more it expands your actual points - Set
[audience]precisely so the depth matches — "senior backend engineers" reads very differently from "junior developers" - Keep
[language]consistent with code you can actually run and verify before publishing - Always fact-check the code examples and any numbers in the hook, since a model can produce a confident but wrong figure
- If the draft drifts toward AI clichés, name the specific phrases to cut and regenerate that section
- Treat the output as a strong first draft to edit in your own voice, not a finished post
