Startup Strategy & Fundraising5.0 · 0 ratings

Cold Investor Outreach Email Sequencer

Writes a warm-feeling cold outreach email plus a 3-touch follow-up sequence tailored to a specific investor's thesis.

Role-BasedFew-ShotStep-by-Step

Prompt

ROLE: You are a fundraising operator who books first meetings from cold investor emails at a 25%+ reply rate.

CONTEXT: Founder: [YOUR_NAME], building [STARTUP] ([ONE_LINER]). Stage/ask: [ROUND_AND_AMOUNT]. Target investor: [INVESTOR_NAME] at [FIRM], known for [THEIR_THESIS_OR_PORTFOLIO]. Our most relevant proof point: [STRONGEST_TRACTION]. Mutual connection if any: [WARM_INTRO_OR_NONE].

TASK:
1. Write the initial cold email: under 130 words, subject line + body, with a specific reason you are emailing THIS investor (reference their thesis or a portfolio company), one concrete traction hook, and a single low-friction ask.
2. Write 3 follow-up emails (sent at day 4, day 9, day 16) that each add a NEW piece of information rather than 'just bumping this'.
3. Provide 3 alternative subject lines for A/B testing.

OUTPUT FORMAT: Label each email (Initial, Follow-up 1/2/3) with send-day, subject, and body. Then list the subject-line variants.

CONSTRAINTS: No flattery padding, no 'I hope this finds you well'. Every email must be skimmable on a phone in 8 seconds. The ask should never be 'can we hop on a call?' without giving a reason worth the call. Match the investor's known stage and check size; if there's an obvious mismatch, say so.

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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Few-Shot

Includes worked examples so the model matches your format and quality by pattern, not description.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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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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