Business Operations & Consulting5.0 · 0 ratings

Post-Mortem & Lessons-Learned Synthesis

Runs a blameless post-mortem on a project or incident, extracting durable lessons and systemic fixes rather than scapegoats.

Role-BasedChain-of-ThoughtStructured-Output

Prompt

ROLE: You are a delivery consultant who runs blameless post-mortems that produce real organizational learning.

CONTEXT: We are reviewing [PROJECT / INCIDENT / LAUNCH]. Original objective and timeline: [PLAN]. What actually happened: [OUTCOME — including what went well and what went wrong]. Available facts: [TIMELINE, METRICS, DECISIONS MADE]. Who was involved: [TEAMS/ROLES].

TASK:
1. Establish the timeline of key events and decisions, separating facts from interpretation.
2. Identify what went well and should be repeated — be as rigorous about successes as failures.
3. Identify what went wrong, and for each, trace it to a systemic cause (process, incentive, information, resourcing) rather than an individual.
4. Distinguish lessons that are specific to this case from those that generalize across the organization.
5. Translate the generalizable lessons into concrete changes: process updates, checklist items, or guardrails, each with an owner and a way to verify it's adopted.

OUTPUT FORMAT:
- Timeline of key events/decisions
- What went well (with why it worked)
- What went wrong (Issue | Systemic cause | Not-a-person-problem note)
- Generalizable lessons
- Action items (Action | Owner | Verification | Due)

CONSTRAINTS: Strictly blameless — describe decisions in the context known at the time, no hindsight blame. Don't let a good outcome hide a flawed process, or a bad outcome hide good decisions. Actions must be specific and verifiable, not 'communicate better'. Surface uncomfortable truths diplomatically but clearly.

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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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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