How to Create Custom GPT Models

1. Introduction

With the public launch of ChatGPT by OpenAI in 2022, we’ve had an outpouring of AI applications and platforms. These platforms greatly aid and improve research and automation of relatively simple tasks. Consequently, with the launch of newer GPT models, we see the adoption of AI aiding much more complex tasks in healthcare, engineering, and sciences.

Custom GPTs are the next frontier in AI-driven solutions, and with the increasing computational power of GPT models, their potential is limitless.

In this tutorial, we’ll look at how we can build these custom GPT models tailored to help businesses, researchers, and developers address specific needs.

2. What Are Custom GPTs?

Custom GPTs are personalized versions of OpenAI’s large language models, like GPT-4, that are fine-tuned or configured to handle specific tasks. Unlike traditional pre-trained models, these GPTs are modeled for specific workflows, industries, or domains.

Consequently, these custom GPTs provide highly efficient and relevant outputs. For example, an organization could create a custom GPT for technical customer support tasks. Therefore, this GPT model can be trained on the organization’s product documentation and support tickets.

Similarly, we’ve got custom GPTs that researchers train and model using thousands of research documents to summarize research data. The opportunities are endless!

In summary, let’s explore the key differentiators between custom GPTs and traditional pre-trained models:

2.1. Applications of Custom GPTs

Generally, we apply custom GPTs to many industries. Besides the fields we’ve mentioned already, we also see the adoption of custom GPTs in:

• Healthcare: We see the adoption of custom GPTs in automating medical record analysis or even conversational AI models to assist in mental health counseling
• Education: Tailored AI models can assist in personalized tutoring, particularly in subjects like math, sciences, and languages
• Software Development: Automating pull requests, reviewing code quality, and creating documentation are some of the application areas of custom GPTs

Additionally, legal services, finance, gaming, customer support, and human resources are other industries where we see the growing adoption of custom GPTs.

3. Creating Custom GPTs

For this tutorial, we’ll be using ChatGPT’s GPT Builder platform to create custom GPTs. As such, we’d need either ChatGPT+ or ChatGPT Enterprise. This will grant us access to the tools we’d need to achieve this goal.

We also need training material for our GPT model: PDFs, .txt, .csv, and documents about the subject. These documents serve as a knowledge base for our custom model.

Then, we open the GPT Builder. Click the Create button on the Explore GPTs tab.

It looks like starting a new document:

When this opens, the GPT Builder displays an interactive chatbox for instructions on how we want to build our custom model:

This interactive chatbot also previews the results of our custom model in real-time, helping us track changes as they occur. Subsequently, we can input the instructions for our model. Let’s say we want to build a GPT model that simplifies studying for students. We can input instructions:

I want a GPT that explains science topics for school kids like they are 10-year-olds...

Furthermore, we can add more context and modify the behavior of our model based on the builder’s responses. We can also test out our model using the Preview tab for refinement:

The system adjusts itself automatically based on the feedback it gets from us in this tab. Our interactive bot also suggests the name, description, and other information for the GPT. As you can see from the image, the name we’ve chosen to run with for our GPT is Science Buddy.

4. Editing Custom GPTs

Alternatively, we can switch to the Configure tab for more customization. This provides more flexibility to choose and set a logo for our model, give it a name, and provide a description:

Generally, we can choose an existing image or create one with AI for our logo. Then, we set up conversation starters to serve as guides to users when querying the model:

Next, we set up our knowledge base by uploading our training material. Since we are building a study guide GPT called Science Buddy, we can upload textbooks, slides, and worksheets that contain accurate and current science information:

Ideally, we want to use documents containing both good and poor answers. This is because this poor information serves as an example of what the model should avoid. An instance of such is a document that says, “Water boils at whatever temperature.”

Let’s test our GPT model by asking it a simple question:

Explain buoyancy and why ships are able to float

Here, the model provides a succinct but understandable response to our query. We can check the response to see if it aligns with our desired tone, accuracy, and other guidelines. Hence, we continue to tweak and update our instructions, prompts, and other information until we’re satisfied with our model’s responses.

5. Enabling Third-Party Actions

By default, the GPT Builder enables Web Browsing and DALL-E Image Generation for newly created GPTs. However, we can set our model to interact with third-party applications outside ChatGPT.

To do this, our custom GPT model would need to communicate with other third-party APIs through Actions. Navigate to Create new action and set the parameters for the third-party API that the model will communicate with:

On the other hand, we can leverage other AI Action services that give our model access to a host of third-party APIs directly from the ChatGPT interface.

6. Sharing Custom GPTs

After refining the GPT model, we save it. Then, we choose how we want to share our newly created custom GPT. There are a couple of ways to do this:

• Invite-only: Only accounts specified with an invite can access the custom GPT
• Anyone with the GPT link: Any account with the custom GPT link can access and use the GPT
• GPT Store: Share publicly and listed on OpenAI’s GPT store. Every ChatGPT user can see, install, and use it for conversations

Conversely, if we use an enterprise plan, we can make our custom GPT model available to anyone within the organization:

An excellent feature of custom GPTs is the ability to modify and retrain at any time. We can always edit our parameters and instructions and even add new actions. Just select the GPT and click Edit GPT. After updating the information, click Update, and it’s done!

We can even go a step further if we want to delete our custom GPT. Click the ellipses (…) on the same page and select the Delete GPT button:

Deleting it removes the custom GPT from the GPT Store if it’s publicly shared and removes access for everyone with the link.

7. Conclusion

Custom GPTs transform traditional models into specialized problem solvers. In this article, we explored how we can create these specialized expert models for any industry.

For starters, we established what they are—the key differentiators from the traditional pre-trained models and their applications in various industries. Then, we discussed how to create, configure, and publish our own custom GPT models using OpenAI’s GPT Builder. Lastly, we learned how we can update and delete these GPTs.