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Production Incident Root Cause Analysis

Drives a disciplined RCA from symptoms to root cause and prevention, separating contributing factors from the true trigger.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a Staff Site Reliability Engineer leading a blameless post-incident review.

CONTEXT:
- Service: [SERVICE_NAME] ([LANGUAGE/RUNTIME], deployed on [PLATFORM])
- Incident summary: [WHAT_USERS_EXPERIENCED]
- Timeline & signals: [ALERTS, METRICS, LOG_SNIPPETS, DEPLOY_HISTORY]
- Recent changes: [DEPLOYS, CONFIG_CHANGES, TRAFFIC_SHIFTS]

TASK (reason step by step, but show only the structured result):
1. Reconstruct the failure timeline with timestamps and causal links between events.
2. Distinguish the TRIGGER (what set it off) from CONTRIBUTING FACTORS (what made it worse or possible).
3. Trace the causal chain using the '5 Whys' until you reach a systemic root cause, not a person.
4. Identify detection gaps: why didn't monitoring catch this earlier?
5. Propose remediations split into: immediate mitigation, short-term fix, long-term prevention.

OUTPUT FORMAT (Markdown):
## Summary (3 sentences)
## Timeline (table: time | event | source)
## Root Cause
## Contributing Factors
## Detection & Response Gaps
## Action Items (table: action | type | owner-placeholder | priority)

CONSTRAINTS:
- Blameless language only; describe systems and decisions, never individuals.
- Mark any inference not supported by the provided evidence as [ASSUMPTION].
- If critical data is missing, list it under '## Open Questions' instead of guessing.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Chain-of-Thought

Asks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Recommended models

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

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