Blank page paralysis kills more projects than bad ideas do. You have the idea, you have the motivation, but you sit down to start and nothing comes out because the path from zero to done feels too long and too unclear. This prompt flips the whole process: describe the finished result, and let AI work backwards to tell you exactly what to build first.
The concept is straightforward. Instead of staring at zero and asking “where do I start,” you start at the end. The author posted this in r/PromptEngineering and the core idea is to force the AI to reverse-engineer the creation process so every step has a clear definition of done. It works because humans are actually pretty good at describing what they want. We’re just not great at sequencing how to get there. This prompt hands the sequencing problem to the AI and keeps the vision work where it belongs: with you.
The Prompt
Here it is, word for word:
“I will provide a description of a finished product. Generate a 7-step plan to create it from scratch. Include: Action, Requirement, and ‘Done’ metric.”
Short. Clean. Surprisingly powerful. Notice it doesn’t ask for advice, brainstorming, or general guidance. It asks for a plan with a specific structure, which is the key reason the output is actually usable instead of just interesting.
Why It Works
Three things make this prompt effective:
- Forced prioritization. Limiting the output to exactly 7 steps cuts the fluff. The AI can’t dump 40 vague suggestions on you. Seven is enough to cover real complexity, but tight enough that every step has to earn its place. If something doesn’t make the cut, it probably wasn’t load-bearing anyway.
- Structured output. Requiring Action + Requirement + Done metric means every step has a trigger, a prerequisite, and a clear finish line. You always know what you’re doing, what you need before you can start, and how you’ll know when you’re through.
- End-state context. Starting from a vivid description of the finished product gives the AI much better material to work with than “help me build X.” The more specific your description, the more specific the plan. A generic input like “build a newsletter” gets you a generic plan. A specific input like “a weekly newsletter that curates three AI tools for non-technical founders, delivered every Tuesday, with a template for each edition” gets you something you can actually act on Monday morning.
The “Done” metric is where most plans fall apart without this prompt. Typical AI responses stay vague about completion. Phrases like “research the market” or “set up the infrastructure” sound like steps but have no finish line. This forces a concrete definition of done for each step, which is exactly what forward momentum requires. You stop second-guessing whether you’re finished and just check the metric.
Use Cases
- 🛠️ Software projects. Describe the finished app or feature, get a structured 7-step dev roadmap. Works especially well for solo builders who need to scope before they start, not after they’re already three tangents deep.
- 📄 Content builds. Describe the finished course, newsletter series, or video library. If you know what the complete product looks like, you can back into a production schedule that doesn’t collapse in week two.
- ⚙️ Business systems. Describe the finished SOP or workflow, get back a sequenced build plan. This is useful any time you’re trying to document or systematize something that currently lives only in someone’s head.
Two Variations Worth Trying
Want sharper results? Try adding a role at the front:
“You are a senior product manager. I will provide a description of a finished product. Generate a 7-step plan to create it from scratch. Include: Action, Requirement, and ‘Done’ metric.”
The role primes the model to think in terms of dependencies, handoffs, and sequencing rather than just listing obvious tasks. You’ll notice the steps get more precise about order and less generic about execution.
Or add a constraint to force lean thinking:
“I will provide a description of a finished product. Generate a 7-step plan assuming a solo founder with no budget. Include: Action, Requirement, and ‘Done’ metric.”
The constraint version especially changes the quality. It stops the AI from giving you ideal-world steps and forces it to think about what’s actually buildable. Steps like “hire a designer” disappear. Steps like “use a free Figma template to validate the layout before writing a line of code” show up instead. That’s the difference between a plan you can start today and one that requires conditions you don’t currently have.
Prompt of the Day
Copy this and try it on something you’ve been putting off:
“I will provide a description of a finished product. Generate a 7-step plan to create it from scratch. Include: Action, Requirement, and ‘Done’ metric.”
Describe the finished version of whatever project is sitting in your backlog. Be specific. The more vivid the end state, the better the plan. A good end-state description covers what it does, who it’s for, what format it lives in, and roughly how it gets used. Two to four sentences is usually enough. If you can’t describe the finished thing clearly yet, that’s worth knowing before you start building.
See It in Action
Head over to the original post in r/PromptEngineering to see other practitioners discuss how they’re adapting this. Short prompts like this one tend to generate the most useful variations in the comments because the base prompt is simple enough that everyone immediately starts testing it on their own context. That’s where the interesting edge cases show up, including people using it for things like event planning, hiring pipelines, and physical product launches. Worth a read before you settle on your own version.
How to ‘Jailbreak’ your own creativity (without breaking rules).
by u/Significant-Strike40 in PromptEngineering