If you have ever used ChatGPT or any similar large language model (LLM), you know that it contains a vast array of knowledge from around the globe. When you type a prompt, it provides an answer.
However, these LLMs do not always contain information specific to your company, book, newspaper, or blog. In cases like this, you might want to create a GPT. Here are some example use cases:
Examples of GPTs you might consider creating.
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Don't know what a GPT is? Please read my article The History of the GPT in ChatGPT.
Introducing Custom GPTs
While there are some nuances here, there are three types of GPTs you can create:
- 🧱 No-code GPTs : Ideal for simple chatbots, FAQ bots, and essential customer service assistants.
- 🎨 Low-code GPTs : Suitable for moderately complex applications such as task automation bots, integration with CRM systems, and personalized recommendation systems.
- 🧑🏽💻 Full-code custom GPTs : Best for advanced applications like custom enterprise solutions, highly specialized virtual assistants, or intricate data processing bots.
In the remainder of this article, I will describe each type of custom GPT. In future articles, I will expand on these types with more detail. Want to know more? Please share your thoughts in the comments or book a 30-minute meeting with me to ask more questions about your use case.
🧱 Introducing no-code GPTs
No-code GPTs are like building blocks; you upload knowledge and direct the GPT to answer various prompts.
You probably have a corporate portal that uses SharePoint or Notion. You also have Slack or Teams with dozens or hundreds of channels. Finding something as simple as vacation days or holidays can be a painful exercise, especially for new employees. Let's use that simple example to create a no-code GPT. Later in this article, I show you how low-code and full-code GPTs can take this capability to the next level.
The problem
Let's say you go to ChatGPT, Copilot, or some other LLM and type the following prompt: How many vacation days do I have? The response will be something like the one you see in the following image: it does not know your company's policy.
ChatGPT and other LLMs cannot answer questions unique to your own business, such as "How many vacation days do I have?"
The no-code solution
You can create a GPT that understands your company's corporate leave policies. As part of that process, I will make a new no-code GPT and upload our internal Company Corporate Leave Policy in PDF format.
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At the time of this writing, ChatGPT and Microsoft Copilot provide an easy-to-use interface for creating personal, team-based, or public no-code and low-code GPTs. Google's Gemini and others have similar functionality, but I believe they need more time before they are ready for production use.
Prepare your GPT's "knowledge."
The Company Corporate Leave PDF document I uploaded contains pictures and special formatting, as shown in the following image. The LLM can still read and make sense of the document.
A corporate leave policy document in PDF form.
Finalize the no-code GPT
Next, I will finish configuring the no-code GPT by letting ChatGPT or Copilot create a logo and some sample questions to guide the user through its use. The following image is a screenshot of my new Corporate Leave Policy GPT.
The corporate leave policy has been uploaded to ChatGPT. Sample questions and even a logo were automatically generated.
Use the no-code GPT
When employees want to ask about corporate leave policies, they go to the GPT rather than send a message to an HR employee.
In the following image, I prompt the no-code GPT with How many vacation days do I have? This time, the GPT responds with the correct answer, which is 15 days. The GPT knows this based on the previously uploaded knowledge document (the Corporate Leave Policy PDF.)
Using the GPT, an employee can ask how many vacation days they have and receive an accurate response based on the corporate leave policy document.
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Most LLMs (ChatGPT, Copilot, Gemini, Claude) can read popular PDF, Word, PowerPoint, HTML, and markdown formats. If you want to create your own GPT, you should start by uploading a simple document to ensure it is a supported file format.
🎨 Introducing low-code GPTs
A low-code GPT is like paint by numbers because it provides a guided structure with room for creativity and customization.
Sometimes, responding to a question based on a document is not enough. Let's continue with our example of using a corporate leave policy. As you may be aware, when you work for a company, you might have more vacation days based on seniority, such as the number of years you have worked, your pay grade, or other criteria. That information is typically stored in a database; your IT department may have easy ways to connect to that database using Application Programming Interface (API) connections.
Let's see what happens if a software developer—or proficient technical person—writes a small amount of code for our policy leave example. I will use the original GPT containing the generic corporate leave policy again. This time, our low-code GPT will access the HR database's API and give the employee a more personalized response experience.
As you can see in the following image, my prompt How many vacation days do I have? results in a response based on my seniority within the company. Not only that, but it also shares information from the generic policy that is available to everyone.
A low-code GPT informs the user of how many vacation days they have based on their seniority.
Your low-code solution is now more powerful because you combined it with a database that provides more value to the employee.
🧑🏽💻 Introducing full-code custom GPTs
A full-code GPT is like crafting something from scratch because it allows you to design and build every aspect from the ground up like an artisan would start using raw materials.
Sometimes, you want to create something people can use outside your company, such as an app on a phone or a bot to interact with your business. You may not want to direct someone to ChatGPT or Copilot in those cases. Instead, you want to create a personalized and branded experience. If that is the case, you will have software developers create the app using code that connects to ChatGPT using APIs – or application programming interfaces. With a full-code GPT, you can make a fully customized user experience.
Let's continue with our corporate leave policy example. This time, I created a full-code custom chatbot named "Jobsy" and placed it on my company's Job Listings page. Jobsy will engage job seekers to answer questions about what it is like to work there and ask HR policy questions.
In my example, the job seeker asks the Jobsy bot If I take a job, how many vacation days will I receive? As you can see in the following image, Jobsy responds with an answer and encourages the job seeker to ask more questions or check out the job listings.
A full-code GPT can be a website chatbot, a phone app, or any other form you might imagine.
Full-code GPT solutions are compelling but will take time. You might find a vendor that already provides a solution, or you will hire a software developer(s) to build something from scratch. You can even create an app you sell on an app store that uses ChatGPT or other LLMs in the background. The ideas are limitless here, but some may include games, gardening support, data visualizers, or more.
Want to discuss this further?
If you want to create a GPT for yourself or your business, book a 30-minute call with Bill Raymond using the link below:
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30 Minute Meeting - Bill Raymond
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Dr. Deborah Chen provided editing services for this article.
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