Increase revenue: Create a GPT that understands your business, customers, and goals

The AI research blueprint every entrepreneur needs

72% of entrepreneurs say they’ve wasted time and money building an AI tools they can’t use.
(Source: McKinsey + OpenAI Startup Report, 2024)

The main reason? They trained their CustomGPTs on the wrong data.

Random blog posts. Outdated PDFs. Scraped content with no context. And then they wonder why the output is generic, inaccurate, or just plain unhelpful.

If you’re serious about using ChatGPT (or Gemini, when needed) to build a CustomGPT that people trust, you need to be just as intentional with your data as you are with your product.

This post will walk you through a 5-step system to source and prepare expert-level training data—so your GPT gives answers that are accurate, relevant, and differentiated.

Step 1: Define your model’s mission and niche topics with precision

Before you touch a line of code or start collecting sources, you need clarity. A CustomGPT with a vague mission will always produce vague results.

This is where most people fail. They try to build something broad—like a “business advice GPT”—and end up with something so diluted it’s useless.

Instead, get specific. Try something like:

“A GPT that helps SaaS founders analyze and negotiate term sheets based on the last 5 years of venture capital deal trends.”

That’s focused. That’s valuable. That’s trainable.

To do this, create a Domain Definition Document (DDD). It should outline:

• A one-sentence mission for your GPT

• A list of key topics and subtopics the model should master

• Clear boundaries of what the GPT should not cover (e.g., “no non-U.S. contracts” or “no tax advice”)

• A description of the ideal source: someone like a tenured VC, startup lawyer, or experienced founder

This is the foundation for everything else you’ll do.