AI still doesn’t write usable copy. It’s verbose and either overly formal or casual. People’s internal AI-detection algorithms are already well-tuned to sniff it out. This is particularly true with cold emails. People are already wary of you. But AI is good at writing tightly constrained variables. You can use these variables inside templates written by humans to make them feel fully personalized to get responses like this: A book with the title 'The Cold Email Handbook'

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Here’s how to use AI to make cold emails that work

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#1. Pick a specific dream customer.

Draft a specific email for them. This is a forcing function to write a great email you’ll use as a template.

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#2. Break down the dream email.

Identify the static parts (your name, company, etc) versus the parts that should be customized for each person. Don’t just constrain yourself to boring variables like company name. Instead, think:
  • Common pain points your customers experience
  • “Jobs to be done” of your product or their product
  • Summary of LinkedIn posts or company/personal bios
  • Their current goals as an organization (fundraising, scaling a channel, etc)
For example: A book with the title 'The Cold Email Handbook' View fullsize The screenshot is showing a programmatic email template that references things like open sales roles, how many reps, what fundraising stage the company is at, what their dream ICP looks like, what the hair on fire moment is, and more.

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#3. Use the 3 C’s to build prompts to fill in the blanks.

  • Context: Give ChatGPT the context it needs to know what to do.
  • Creative Constraints: Tell it exactly what you want the output to look like (x words or less, casual, lowercase, no quotes, etc.)
The data you feed into the prompts can come in two forms:
  1. Explicit data:
    1. Data from LinkedIn, like a company’s description or a post.
    2. Scraped data from company pages, job boards, fundraising databases, etc.
  2. Inferred data: Input for a prompt can be the output from another earlier prompt. For example, for the first email in Aurora’s outbound campaign (example above)
    1. Use the LinkedIn company bio + scraped homepage as inputs to build a “dream ICP” output from ChatGPT.
    2. The ICP output (and the company bio + scraped homepage context) can all be used as inputs to another prompt to output a “pain moment” output.
    3. Both are used as inputs to determine what companies should look for in their prospects’ job posts and whether they need their service.
What the above template looks like when you get AI to fill in the blanks (read my post on The Right Way to Use AI in Cold Email for example prompts used): A book with the title 'The Cold Email Handbook' A book with the title 'The Cold Email Handbook'
^ Notice this doesn’t have a CTA. I’m just sharing an idea for free. Not trying to close a sale immediately. It warrants a response since it doesn’t feel like a pitch.
If Mutiny or LexCheck want to run with the idea themselves, cool. If they want to learn more and maybe work with our agency, even better. If you need help getting started, join our community discord and ask for help. We’ve got your back. And if you’re ready to rock and roll, sign up to Za-zu, the most enjoyable and effective way to outbound.If you need help getting started, join our community discord and ask for help. We’ve got your back. And if you’re ready to rock and roll, sign up to Za-zu, the most enjoyable and effective way to outbound.