Strategy5.0 · 187 ratings

Pre-Mortem — Imagine We Failed

Run a pre-mortem on a decision. Imagine it's failed; trace back the most likely cause.

Role-BasedChain-of-ThoughtOutput-Format

Prompt

**Role:** Strategy advisor who has facilitated 30+ pre-mortems for startup teams. You know how to make people honest about risks they're emotionally invested in.

**Context:** Decision / project: [what we're planning]. Timeline: [when we'll know if it worked]. Stakes: [what's at risk — team time, runway, brand]. Team's emotional investment in this working: [high/med/low].

**Task:** Run the pre-mortem.

1. Frame: "It's [end date]. The project failed. What happened?"
2. Imagined failure scenarios: list 5-7 specific ways this could fail. Be uncomfortable. Include scenarios the team would push back on.
3. For each scenario: rank by likelihood (low/med/high) and severity (low/med/high). Compute risk score.
4. Top 3 risks: for each, name the EARLY indicator that the scenario is starting to play out. The signal we'd see in 30 days, not at the end.
5. Mitigation: for each top-3 risk, the specific action we'd take to prevent OR detect-and-pivot.
6. Kill criterion: name the ONE thing that, if it happens, means we should stop and re-plan.

**Constraints:**
- Failure scenarios must be specific (not "execution issues" but "two key engineers quit by month 3")
- The team's preferred narrative is the wrong starting point — push past it
- Every mitigation has an owner and a check-in date
- The kill criterion is non-negotiable — must be specific enough to be observable

**Output format:** 5 sections · risk matrix · top-3 deep dive · kill criterion callout.

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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Output-Format

Specifies the exact shape of the result — sections, a table, JSON, a word count — so the output is predictable and ready to use.

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