Raw brainstorming sessions should end with clarity. Instead, they usually end with a wall of scattered text that takes another hour to untangle.
You ask an AI to turn your notes into a mind map. It gives you a bulleted list with inconsistent indentation that breaks the moment you try to import it into Xmind or MindNode. So you spend more time cleaning up the output than you would have building the map yourself. The indentation is wrong on half the nodes. Some concepts that belong together end up on opposite sides of the hierarchy. The top-level branches read like a table of contents someone wrote in a hurry. It is one of those small frustrations that compounds fast, especially when you are in a flow state trying to process a big idea and the tooling keeps interrupting you.
A Redditor named u/keyonzeng found a way to fix this, and the fix is more principled than I expected.
The Information Architect Approach
The post author built a prompt that reframes the entire task. Instead of asking AI to “make a mind map,” the prompt assigns the model a specific role: information architect. Then it enforces strict output constraints to ensure the result is actually importable by mapping software.
The key mechanism is called Skeleton Extraction. The prompt instructs the model to:
Analyze all input materials to identify the most generalized core logical framework, using this as the L1 and L2 backbone nodes.
That single instruction changes everything. The AI builds the tree from the trunk outward, not from scattered leaves inward. Top-level branches reflect actual core logic, not surface-level topics. The output uses proper Markdown headers that map directly to depth levels in your mapping software. H1 becomes the root node. H2 becomes first-level branches. H3 becomes sub-branches. No ambiguity, no guessing, no manual reformatting. What you paste into Xmind is exactly what you mapped in your head, just organized correctly for the first time.
The role assignment matters as much as the output format. When you tell a model it is an information architect rather than a note-taker, it approaches the material with a different priority: structure over completeness. It asks which concepts belong at the root and which are derivatives. That shift in framing consistently produces tighter hierarchies than any amount of post-hoc editing.
Old Way vs New Way 🔄
- 📋 Vague request: “Turn my project notes into a mind map” → inconsistent nesting, broken imports, 45 minutes of manual indentation fixes
- 🏗️ Structured prompt: “Extract core framework, map scattered details to nodes, output strictly following header syntax” → deep hierarchy, Xmind-ready, zero cleanup
The difference is not the AI’s capability. It is whether you hand it an outcome to guess at or a process to follow. Process requests produce consistent, parseable structure. Outcome requests produce whatever the model decides looks like a mind map. Think of it like giving directions: “get me to the airport” gets you a route that makes sense to the driver, while “take I-95 North, exit 42, terminal B” gets you exactly where you need to go. The specificity of the instruction determines the reliability of the output.
How to Apply This Prompt
- Paste your raw brainstorming notes as the input material. Do not clean them first. The prompt is designed to handle mess, and pre-sorting the notes often introduces your own biases about structure before the AI has a chance to find the real skeleton.
- Let the AI run the Skeleton Extraction step first to define L1 and L2 backbone nodes. Treat this as a draft, not a final product.
- Review the top-level branches before accepting: do they reflect your actual core logic or just surface-level topics? If a branch label sounds like a chapter title from a generic textbook, push back and ask the model to go deeper.
- Copy the Markdown output and save it as a .md file. Keep the original notes file too so you can cross-reference if anything got lost in extraction.
- Import directly into Xmind, MindNode, or any Markdown-compatible mapping tool. Most tools accept .md files natively via File > Import.
The full prompt is available via the link in the original Reddit discussion. Two quick variations worth trying: (1) pass meeting notes instead of brainstorming notes to generate a decision hierarchy, and (2) run it on a long article outline to expose where your argument has gaps before you write. A third variation that works surprisingly well: feed it a job description and your resume to see how your experience maps to what the role actually requires.
The Real Payoff
The original poster made an observation worth sitting with: seeing the hierarchy usually exposes where your logic is thin. Four branches under “strategy,” zero under “execution.” The structure forces honesty about gaps that a flat document hides. A Google Doc full of paragraphs can look complete even when the reasoning is circular or a whole category is missing. A mind map with an empty branch is impossible to ignore. The visual weight of an underdeveloped node is immediate and uncomfortable in the best way.
This is the deeper principle behind the prompt. The information architect persona does not just clean up formatting. It surfaces the shape of your thinking so you can see what is missing. The tool becomes a mirror for your reasoning, not just a formatter for your words. That is a different category of useful entirely.
Head over to the original r/PromptEngineering discussion to grab the full prompt and see what others are building on top of this approach!
[Productivity] Transform raw notes into Xmind-ready hierarchical Markdown
by u/keyonzeng in PromptEngineering