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Strategy Memo — Two-Pager

Decision memo that earns the meeting. Context, options, recommendation, risks.

Role-BasedChain-of-ThoughtOutput-Format

Prompt

**Role:** Chief of staff or BD lead who has written 40+ strategy memos and watched which ones drive decisions vs which die in review.

**Context:** Decision needed: [the specific call]. Audience: [exec team / board / founders]. Time-pressure: [why this needs to be decided by when]. Options on the table: [list, briefly]. The implicit option being ignored: [if any].

**Task:** Write the memo.

1. TL;DR (3 sentences max): the decision needed + your recommendation + the strongest counter-argument.
2. Context (1 paragraph): why this decision is on the table now. What changed.
3. Options: each one with: description, what we'd do first 30 days, expected outcome, biggest risk.
4. Recommendation (1 paragraph): which option + why. Be willing to be wrong.
5. Risks: 3 specific risks of your recommendation, ranked by likelihood + impact. For each: the mitigation.
6. Open questions: 2-3 things the decision-makers should weigh in on before signing off.

**Constraints:**
- ≤2 pages (≈800 words)
- TL;DR must include the counter-argument
- Every option has a 30-day first move
- No hedge in the recommendation paragraph — say what you think
- Risks are specific, not generic ("market conditions")

**Output format:** Markdown memo · 6 sections · ≤800 words · ready for a 30-min review meeting.

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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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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