Startup Strategy & Fundraising5.0 · 0 ratings

Target Investor List Builder And Prioritizer

Generates a prioritized, segmented investor list with fit rationale and the best warm-intro path to each.

Role-BasedStep-by-StepStructured-Output

Prompt

ROLE: You are a fundraising research lead who builds tiered investor target lists that maximize meeting-to-term-sheet conversion.

CONTEXT: Company: [COMPANY], a [STAGE] [SECTOR] startup. Round: [AMOUNT] at [VALUATION_OR_CAP]. Geography: [LOCATION]. Notable traction: [TRACTION]. Existing investor/advisor network: [PEOPLE_WHO_CAN_INTRO].

TASK:
1. Define the ideal investor profile: stage, check size, sector focus, geography, and a 'must-not' filter (e.g., funds with a competing portfolio company).
2. Organize targets into three tiers - Tier 1 (dream-fit leads), Tier 2 (strong fits), Tier 3 (fillers/momentum) - and explain what qualifies a fund for each tier.
3. For each tier, describe the outreach approach and the ideal sequencing (who to pitch first to build momentum vs. saving the best lead).
4. Map warm-intro paths: for the profile of investor I should pursue, what kinds of mutual connections to look for and how to ask for the intro (give an intro-request blurb template).

OUTPUT FORMAT: (1) Ideal investor profile + must-not filter; (2) Tier definitions and outreach approach per tier; (3) Recommended sequencing strategy; (4) Warm-intro request template.

CONSTRAINTS: Prioritize fit over brand prestige - a perfect-stage fund beats a famous mismatched one. Warn against pitching your top choice first before the pitch is battle-tested. Do not fabricate specific fund names or partner details; work from profiles unless I provide names.

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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Step-by-Step

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