Most of us don’t struggle with coming up with ideas; we struggle with the friction of getting an LLM to execute them properly. You know the drill: you type a request, get a generic response, and then spend the next hour refining constraints and fixing edge cases. This savvy professional got tired of that infinite iteration loop and built a solution to automate the heavy lifting.
The Twist: Context Packages, Not Just Prompts
What makes this tool interesting is that it doesn’t just try to write a “better” prompt using flowery language. Instead, the creator designed it to generate a comprehensive context package.
When you provide a rough idea, like “Build a subscription SaaS for fitness coaches,” the tool doesn’t just expand the sentence. It constructs a structural blueprint that forces the AI to think like a senior engineer. It shifts the focus from “creative writing” to “specification engineering.”
I think this changes how we approach prompting because it acknowledges that LLMs need rigid boundaries to be truly useful for coding and building.
What It Actually Builds
According to the post’s author, the tool takes your one-liner and expands it into a full execution plan. Here is what the output includes to ensure the AI stays on track:
- Clear System Role: Defines exactly who the AI is supposed to be.
- Objectives & Success Criteria: Tells the AI what “done” looks like.
- Constraints & Guardrails: crucial for preventing the AI from hallucinating features you didn’t ask for.
- Edge Cases: Pre-emptively handles scenarios that usually break code (like what happens when a user has no data).
- Data Structure Suggestions: It outlines how the data should look before writing the logic.
- Failure-State Handling: Instructions on what to do when things go wrong.
The Workflow
If you want to try this approach based on the creator’s methodology, here is how the flow works:
- Input: You start with a raw, messy idea. Example: “Build an AI cold email generator for B2B agencies.”
- Process: The tool analyzes the intent and fills in the missing technical gaps.
- Output: You receive a structured block of text (the context package).
- Execution: You paste that entire block into ChatGPT, Claude, or Gemini.
Why This Matters
The innovator behind this project points out that this is specifically built for indie hackers and agencies who ship weekly. The goal isn’t to replace your creativity but to reduce the “iteration chaos” that happens when you have to explain basic requirements five times.
By handing the AI a pre-chewed structure including output formats and step-by-step execution plans, you effectively skip the first four rounds of bad code generation. It’s about getting a usable result on the first try.
Pro Tip
The author notes that this is currently in a testing phase. If you decide to check it out, pay attention to the “Edge Cases” section of the output. In my experience, this is usually where AI coding assistants fail hardest, so having a tool that generates those upfront is a massive time-saver. 🚀
You can find the discussion and details on how to test the tool in the original Reddit thread.
I built a tool that turns rough feature ideas into build-ready AI instructions (no CKO engineering needed)
by u/Ok-Display5856 in PromptEngineering