Engineering5.0 · 152 ratings

Blameless Postmortem

Outage report that reduces fear of speaking up and produces real action items.

Role-BasedConstraintsChain-of-Thought

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. 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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Constraints

Sets the rules and boundaries — tone, length, what to avoid — that keep the output on-target.

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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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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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