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The first answer is almost never the real cause. Most organizations stop at Why 2 or Why 3 and fix the symptom instead of the source. Run this drill separately for each distinct symptom you identified during problem scoping.
Your Root Cause Drill
Symptom: _________________________________
| Why | Question | Answer |
|---|---|---|
| Why 1 | Why is this happening? | |
| Why 2 | Why is that happening? | |
| Why 3 | Why is that happening? | |
| Why 4 | Why is that happening? | |
| Why 5 | Why is that happening? |
True root cause: _________________________________
Fix direction: _________________________________
The Rule
If your Why 5 answer still sounds like a technical fix, you haven’t gone deep enough. The true root cause is almost always a governance gap, ownership gap, design assumption, or process failure — not a model failure.
Quick Reference — Common Patterns
| Surface Symptom | Likely Root Cause at Why 4–5 | Fix Direction |
|---|---|---|
| Confident wrong answers | No grounding — model using memory, not documents | RAG implementation |
| Outdated information | No knowledge base ownership or update process | Governance and process |
| Accuracy declining over time | Concept drift — real world changed, model didn’t | Retraining + monitoring |
| Vague or unhelpful answers | Domain mismatch — wrong knowledge base for this user | Prompt and retrieval layer |
| Different outcomes for same inputs | Historical bias encoded in training data | Fairness audit + legal escalation |
| Works in testing, fails in production | Test data not representative of real users | Test set redesign |
| Inconsistent answers | Temperature too high or no grounding | Configuration + RAG |
Found your root cause? Confirm your fix will actually be adopted with The 15-Minute AI Fix Viability Scorecard, or read the full AI Model Diagnostic Framework for the complete method.