More Maine businesses are using AI to get consistent, high-quality work done faster. But anyone who’s typed a prompt into ChatGPT knows that AI responses are often too generic for real business use: the tone feels off, it doesn’t address your customers’ pain points and it rarely understands your processes or expertise.
That’s where an AI assistant — often called a custom GPT — comes in. Instead of a simple prompt, a custom GPT is an AI assistant built to your business specifications. You’ll train it on your brand voice and customer data. You provide it with specific instructions, helping deliver better outputs and outcomes for your business.
Most AI platforms offer the ability to build AI assistants at the paid tier. ChatGPT calls them Custom GPTs, Claude calls them Projects and in Google’s Gemini they’re known as Gems. All work almost the same way, so here’s how to start building your first AI assistant:
Start with a clear problem you want it to solve
A custom GPT becomes truly useful when you define what specific outcomes you need, whether it’s to automate recurring tasks; repurpose long-form content into social media snippets; or create custom proposals based on specifics from a sales call.
Think of the problem like a job description: what exact tasks do you want this assistant to handle regularly? That will guide the rest of the setup.
Build the right foundation
A custom GPT is built on two fundamental inputs:
- Instructions: This is your rulebook. You tell the AI how it should behave, what it should prioritize, what tone to use, and even what it shouldn’t do.
- Knowledge documents: These are the materials your GPT can reference when answering queries. They might include things like your brand voice guide, buyer personas, internal documents, style guides, and successful past content you want it to replicate.
There are two knowledge documents I always include:
- Brand voice document: You can create this by feeding samples of your writing (blogs, newsletters, social posts, web copy) and asking the AI to summarize your tone, style and preferred language.
- Ideal client persona: This outlines real audience pain points, preferred messaging, decision drivers, and typical objections.
Providing these documents helps your GPT understand your brand, your audience and cuts down on any hallucinations.
Treat instructions and prompts differently
Instructions and prompts are not the same thing. Instructions are the operating system for your custom GPT. They define identity, tone, limits, and defaults, like a style guide plus standard operating procedures. Prompts are one-off requests you give the GPT during use (i.e., “Summarize this transcript and turn it into a blog draft”).
Think of it like this: instructions are like an NFL rulebook; it lays down how the game is played. Prompts are like an individual play call; they are unique to the situation, but they still need to follow the rule book.
Test, iterate and refine
You won’t nail your custom GPT on the first try; it takes some experimentation and refinement.
Once you upload instructions and docs, try real-world tasks you expect the GPT to handle, review and rate its outputs; update your instructions based on what didn’t work; and add or revise knowledge docs to fill gaps.
Iteration is your secret weapon. Every tweak sharpens accuracy, relevance, and reliability — which turns your GPT from a curiosity into a tool that teammates actually use.
The takeaway
Custom GPTs aren’t a magic bullet, but they can scale your team’s expertise, maintain consistency and free up people’s time to do more valuable work.
The core steps are straightforward: define a real use case, feed it structured instructions and knowledge docs, tell it how you want it to behave, and then continually refine.
Once you start firing up your own GPTs, you may wonder how you ever survived without them.