The Semantic Tree

What it does

semantic-tree turns a broad topic into a navigable structure before asking AI to collapse it into an answer. It separates the branches that can change the decision, connects evidence and assumptions to the right branch, and ends with the few next choices that have the most leverage.

When to reach for it

Use it when a question feels bigger than the first answer, several ideas are tangled together, or research has produced more notes than direction. Skip it when the work is already narrow and the next action is obvious.

Prerequisites

Bring one topic, decision, or rough idea. No software setup is required.

The workflow

1. Name the central question in one sentence.
2. Separate it into decision-changing branches.
3. Attach evidence, constraints, assumptions, and unknowns.
4. Connect dependencies and conflicts across branches.
5. Collapse the tree into the next three decisions or actions.

Common questions

Is this just a mind map?

No. A mind map can capture everything associated with a topic. A Semantic Tree keeps only the branches that change the answer and labels the evidence and uncertainty behind them.

Does every tree need a diagram?

No. Use Markdown when the hierarchy is enough. Use Mermaid only when the relationships are materially easier to understand visually.

It's working if

• The root is one real question rather than a broad subject label.
• Each branch can change the answer or the next action.
• Evidence and assumptions are visibly different.
• The result ends with fewer, clearer decisions than the original prompt.

Where it fits

Use semantic-tree before research, planning, or a consequential decision when you need to understand the shape of the question first. Its output can feed research, grill-me, to-spec, or a direct decision.