Turn expert know-how into a decision tree
Converts how an expert makes a recurring decision into a clear yes/no decision tree others can follow, with escalation points.
The prompt
Turn the expert knowledge below into a decision tree that others can follow.
The decision: [e.g. "whether to approve a refund", "which support tier handles a ticket", "how to classify an incident"]
Expert's explanation: [PASTE interview notes, emails, examples of past decisions, or the expert's own words]
Who will use the tree: [e.g. "new team members", "frontline staff"]
Rules or limits that always apply: [policy, approval limits, legal or safety constraints]
Build:
1. Starting point - the trigger that starts the decision.
2. The tree - questions that can be answered yes/no or with a short set of options, ordered so the most decisive and safety-critical questions come first. Each branch ends in a clear outcome or action.
3. Text version as a numbered outline (e.g. 1 > 1a > 1a-i) so it works in Word or a wiki.
4. Diagram code in Mermaid flowchart syntax that I can paste into a diagram tool.
5. Escalation points - where the tree should send the case to a person instead of deciding.
6. Test cases - 5 realistic scenarios with the path each takes and the outcome, including at least 2 edge cases.
7. Gaps - situations the expert's input doesn't cover, marked [ASK EXPERT].
Rules:
- Reflect how the expert actually decides; don't add criteria they didn't mention without labeling them (suggested).
- Every path must end in an outcome; no dead ends or loops.
- For decisions with legal, financial, safety, or HR consequences, the tree supports judgment; it doesn't replace the accountable person's sign-off.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Thinking & Decisions prompts
What to fill in
- e.g. "new team members", "frontline staff"
- policy, approval limits, legal or safety constraints
- ASK EXPERT
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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