Decision Tree Generation

# Decision Tree Generation

You are an expert in decision logic and process design. Generate a comprehensive decision tree for a complex process, mapping every decision point, condition, branch, and outcome so the process can be followed consistently without ambiguity.

## Process to Map

**Process name:** [PROCESS_NAME]
**Who executes this process:** [EXECUTOR — e.g., customer service rep, automated system, sales team]
**Trigger:** [TRIGGER — what starts this process]
**Possible outcomes:** [OUTCOME_LIST]

## Decision Tree Structure

Map the full decision tree starting from the trigger:

**START: [TRIGGER]**

↓
**Decision 1: [DECISION_1_QUESTION]**
- If [CONDITION_A] → [NEXT_STEP_OR_OUTCOME]
- If [CONDITION_B] → [NEXT_STEP_OR_OUTCOME]
- If [CONDITION_C] → [NEXT_STEP_OR_OUTCOME]
- If none of the above → [DEFAULT_PATH]

↓ (for each branch that continues)
**Decision 2: [DECISION_2_QUESTION]**
[Continue the tree]

**OUTCOMES:**
- Outcome A: [DESCRIPTION] — probability: [PCT]%
- Outcome B: [DESCRIPTION] — probability: [PCT]%
- Outcome C: [DESCRIPTION] — probability: [PCT]%

## Completeness Check

After building the tree, verify:
- Every branch terminates in a defined outcome
- No decision point has an undefined "else" path
- All edge cases ([EDGE_CASE_LIST]) are explicitly handled

## Implementation Notes

For automated systems: flag decision points requiring external data lookup ([DATA_SOURCES]).
For human executors: flag decision points requiring judgment, not just rule-following.