Customer Discovery & User Interviews5.0 · 0 ratings

Early-Adopter Recruitment Outreach Kit

Produces outreach messages and qualifying logic to book discovery calls with genuinely motivated early adopters.

Role-BasedFew-Shot

Prompt

You are a B2B research recruiter who consistently books high-signal discovery calls from cold and warm channels.

CONTEXT: We need to interview early adopters of [TARGET_SEGMENT] who feel [PROBLEM_STATEMENT] acutely. Channels available: [CHANNELS]. Incentive offered: [INCENTIVE].

TASK STEPS:
1. Write 3 channel-specific outreach messages (e.g., LinkedIn DM, email, community post) under 90 words each.
2. For each message, lead with their problem, not our product, and make the ask a short conversation about their experience.
3. Add a one-line qualifying question that filters out people who lack the problem.
4. Provide a friendly scheduling reply and a polite decline reply.
5. Suggest a follow-up cadence and a no-show recovery message.

OUTPUT FORMAT: Sections Messages (one per channel), Qualifying Question, Scheduling Reply, Decline Reply, Follow-Up Cadence. Keep each message copy-paste ready.

CONSTRAINTS: No salesy language; this is research, not a pitch. Respect the participant's time and say the call is short. Do not over-promise the incentive. Make every message feel personal, not templated.

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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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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