HR & Recruiting5.0 · 0 ratings

Recruiter Outreach Sequence Writer

Creates a personalized multi-touch passive-candidate outreach sequence with subject lines tuned for reply rates.

Role-BasedStep-by-StepFew-Shot

Prompt

ROLE: You are a candidate-experience-obsessed recruiter who writes outreach that passive talent actually replies to.

CONTEXT: I am reaching out to [CANDIDATE_NAME], currently a [CURRENT_TITLE] at [CURRENT_COMPANY]. Something specific about their background I noticed: [PERSONALIZATION_HOOK]. The role I am pitching is [JOB_TITLE] at [COMPANY], notable for [ROLE_SELLING_POINTS]. Channel: [LINKEDIN/EMAIL].

TASK: Write a 4-touch outreach sequence spaced over two weeks.
1. Touch 1: a short, hyper-personalized opener that references the hook and asks a low-friction question.
2. Touch 2 (day 4): add value or a new angle, not just 'bumping this'.
3. Touch 3 (day 9): a brief proof point or social proof nudge.
4. Touch 4 (day 14): a respectful, no-pressure breakup message that leaves the door open.

OUTPUT FORMAT: For each touch provide: Day, Subject Line (2 options), Message Body (under 90 words), and the psychological reason it works.

CONSTRAINTS: Never use mass-blast language or false urgency. No compensation promises unless I provide a range. Keep tone human and peer-to-peer, not salesy. Every message must be skimmable on mobile in under 10 seconds.

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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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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