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AI Prospecting
Prompt Library

Generate 30 personalized prospect messages in 35 minutes — without sounding like a bot.

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What This Is: Seven copy-paste AI prompts for every prospecting scenario — cold LinkedIn, email, batch processing, follow-ups, and more. Plus the workflow, personalization framework, and quality controls to maintain 30%+ reply rates while cutting prospecting time by 90%.

warningWhy You Need This

Manual prospecting takes 5–10 minutes per message. That's 2.5–5 hours for 30 prospects. AI can do the research and drafting in 5 minutes — you just polish and send.

These prompts are battle-tested against real outreach results. Each one is structured to avoid the phrases and patterns that get instant deletes, and each includes the exact instructions to produce messages that sound like they were hand-written.

lightbulbHow to Use This Library

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    Read the Workflow: The 4-step process below shows exactly how to go from zero to 30 messages in 35 minutes.

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    Pick Your Prompt: Choose from the 7 templates based on your outreach scenario — each prompt includes what to fill in and what to expect.

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    Personalize: Add 1–2 personalization layers from the guide below. This is what separates human-sounding messages from obvious AI output.

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    Quality-Check Before Sending: Run every message through the 5-point filter. If it fails any check, edit it.

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    Batch & Track: Use the batch workflow and response tracker to scale and optimize over time.

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Tip 1: Build a Swipe File of Winners — When a message gets a great reply, save it. Over time you'll have proven hooks and structures to feed back into your prompts.

Tip 2: A/B Test Subject Lines (Email) — Test 2–3 subject lines on small batches (10 each) before scaling. The right subject line can double your open rate.

Tip 3: Use AI for Replies Too — When prospects reply, use the same prompts to draft your response. Swap "cold outreach" for "reply to this message."

Tip 4: Refresh Prompts Monthly — AI gets stale. Every month, review your reply rates and update prompts based on what's actually working.

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