5 ChatGPT prompt frameworks that produce work you can sell
The gap between output you delete and output you can invoice is almost never the model. It is the structure of the request. These five frameworks cover the vast majority of commercial prompting, and each one exists to fix a specific failure mode.
1. RCTFB — the default structure
Role, Context, Task, Format, Bar. Every serious prompt has all five. Missing Role gives you a generic voice; missing Context gives you plausible but wrong specifics; missing Format means you reformat by hand; missing Bar means you get hype.
Write the Bar as an explicit list of bans: no exclamation marks, no 'unlock', no filler intro, no summary paragraph.
2. Chain of drafts
Ask for an outline, react to the outline, then ask for the draft. One-shot long-form output is almost always mid because the model commits to a structure you never approved.
- Prompt 1: 'Give me three different structural angles for this piece. One line each.'
- Prompt 2: 'Expand angle 2 into a section-by-section outline with the argument of each section.'
- Prompt 3: 'Write section 1 only, to the Bar above.'
3. The critic loop
After the draft, start a fresh request: 'You are a skeptical editor. List the five weakest claims in this text and what evidence each needs.' Then feed the critique back. This one step removes most of the vagueness buyers notice.
4. Few-shot voice cloning
Paste three samples of the target voice, label what makes each one distinctive, then ask for new work 'in the voice demonstrated above, matching sentence-length variance and vocabulary'. Naming the traits matters more than the number of samples.
5. The extraction pass
Instead of generating from nothing, feed the model raw material — call transcripts, reviews, support tickets — and ask it to extract patterns. 'From these 40 reviews, list the ten objections in the buyers' own words, ranked by frequency.' Extraction beats invention every time, because the facts are real.
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Frequently asked
Which framework should I start with?+
RCTFB. It fixes the most common complaint — output that reads generic — and every other framework layers on top of it.
Do these work on other models?+
Yes. They are structural, not model-specific, and work the same on Claude, Gemini and open models.
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