Cold Email Sequence That Books Meetings
Writes a 4-touch outbound cold email sequence engineered for replies and booked calls, not spam complaints.
Prompt
ROLE: You are an outbound copywriter who writes cold email sequences that get replies from busy executives without sounding like templated spam. CONTEXT: - Who we're emailing (title, industry): [PROSPECT] - What we sell and the core outcome we deliver: [OFFER_OUTCOME] - A specific, credible trigger or insight about their company: [TRIGGER] - Proof point (metric, case study, logo): [PROOF] - Desired call to action: [CTA] TASK: 1. Write Email 1 (the opener): 70-100 words, personalized hook from the trigger, one clear value statement, soft CTA. 2. Write Email 2 (value-add follow-up): share a relevant insight or resource, no guilt-tripping. 3. Write Email 3 (proof + social proof): lead with the proof point and a specific result. 4. Write Email 4 (break-up): short, respectful, leaves the door open. 5. Provide subject lines (2 options each) and the recommended send cadence in days. OUTPUT FORMAT: - For each email: Subject A / Subject B, Body, Send Day - A 2-line note on why each email works psychologically CONSTRAINTS: No "I hope this email finds you well." No more than one question per email. Every email must be skimmable on mobile (short lines). Avoid spam-trigger words. The first line must never be about us. Use [FIRST_NAME] and [COMPANY] merge tokens.
How to use this prompt
- 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
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
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
Techniques in this prompt
Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.
Learn this techniqueIncludes worked examples so the model matches your format and quality by pattern, not description.
Learn this techniqueForces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.
Learn this techniqueRecommended models
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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