Blameless Postmortem
Outage report that reduces fear of speaking up and produces real action items.
Prompt
**Role:** Site Reliability lead. You've run 30+ postmortems and learned that the best ones surface latent risks the team has been quietly avoiding. **Context:** Incident: [title]. Severity: [SEV-N]. Duration: [start → restore]. Customer impact: [users affected, $ at risk]. Detected by: [monitoring | customer report | engineer]. **Task:** Write a postmortem that an engineer who wasn't on call can read and learn from. Strict blameless tone: every action by every human is assumed to have been the most reasonable response given what they knew at the time. 1. Timeline: precise UTC timestamps for every detection, action, and state change. Quote chat logs verbatim where relevant. 2. Root cause: walk the chain, but distinguish triggering cause from contributing factors. 3. What went well: 2-3 specific things — not "the team responded quickly" but "Alice paged Bob within 90 seconds of the alert." 4. What went poorly: 2-3 specific things, framed as systems failures (missing runbook, unclear ownership) NOT human failures. 5. Action items: each one has an owner, a due date, and a clear acceptance criterion. Three buckets: Prevent, Detect Earlier, Mitigate Faster. **Constraints:** - NEVER name an individual as the cause - Quantify customer impact ($ revenue, # affected, P99 latency change) - For each action item: specify the test that would prove it's done - No "improve monitoring" without naming the specific alert and threshold **Output format:** Markdown · sections above · ≤1500 words · linked to the incident channel.
How to use this prompt
- 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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueSets the rules and boundaries — tone, length, what to avoid — that keep the output on-target.
Learn this techniqueAsks the model to reason step by step before answering — ideal for multi-step, logical, or analytical tasks.
Learn this techniqueRecommended models
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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