Root Cause CoT Prompt AI Prompt

Design a chain-of-thought prompt that guides an LLM through a data-driven root cause analysis.

Context: given a metric deviation and supporting data, the LLM must reason through possible causes systematically rather than anchoring on the first plausible explanation.

1. The anchoring bias problem:
   - Without explicit CoT, LLMs tend to latch onto the first plausible cause and construct evidence to support it
   - The prompt must force the model to generate and evaluate multiple hypotheses before selecting one

2. Root cause CoT structure:

   Phase 1 — Problem characterization:
   'Before investigating causes, fully characterize the problem:
   - What changed? (metric, direction, magnitude)
   - When did it change? (onset, duration, pattern: sudden vs gradual)
   - Where is it concentrated? (which segments, regions, or products account for the most deviation)
   - What did NOT change? (other metrics that are stable, ruling out systemic causes)'

   Phase 2 — Hypothesis generation (before looking at evidence):
   'Generate 5 possible causes for this deviation WITHOUT evaluating likelihood yet.
   Force yourself to consider: seasonal effects, data pipeline issues, product changes, external events, and measurement errors.'

   Phase 3 — Evidence evaluation:
   'For each hypothesis, evaluate the evidence FOR and AGAINST it from the provided data.
   Be explicit about what evidence would be needed to confirm or rule out each hypothesis.'

   Phase 4 — Hypothesis ranking:
   'Rank the 5 hypotheses from most to least likely. Justify each ranking with specific evidence.'

   Phase 5 — Conclusion:
   'State the most likely root cause. State your confidence level (High/Medium/Low). State the key assumption that, if wrong, would change your conclusion.'

3. Anti-hallucination guardrails:
   - 'Do not cite data that was not provided in the input. If you need data you do not have, say so.'
   - 'If the available data is insufficient to determine the root cause, say so explicitly rather than speculating.'

4. Structured output:
   - The scratchpad contains the full CoT reasoning
   - The final answer is a concise summary: root cause, confidence, key evidence, and next diagnostic step

Return: the root cause CoT prompt, 2 test cases with complete data inputs, expected reasoning chains, and evaluation rubric.