How to Create Your Own GPT Using the OpenAI Interface

In recent years, artificial intelligence, particularly models like GPT, has become an integral part of both our professional and personal lives. Using GPT models streamlines processes, enhances user experience, and fosters innovation across various fields, such as marketing, rapid and efficient code development, intelligent calculations, and more.

GPT models, powered by the OpenAI platform, provide an accessible and straightforward way to create AI-driven systems capable of generating text in an automated and intelligent manner. While you can use the generic GPT to converse on virtually any topic, there are situations where a customized GPT model is more suitable, such as when specific business requirements, privacy concerns, or the need to integrate additional proprietary data arise—data that GPT would not have access to by default.

Let’s take an example: suppose I want to query and retrieve information about transactions and activities occurring on the OpenSea platform (for those unfamiliar, OpenSea is one of the world’s leading NFT trading platforms). If I ask ChatGPT-4o, it wouldn’t be able to provide answers because it lacks access to that specific data. To solve this access issue, there are several approaches available, from using RAG (Retrieval-Augmented Generation) to add extra context to prompts, to developing a customized GPT model with direct access to the system we wish to query.

The goal of creating your own GPT is to offer maximum flexibility in tailoring technological tools to meet your business needs, improving workflows, saving resources, and maintaining full control over your data and information.

Here is an easy way to create your own GPT through OpenAI

1. Configure new GPT using openAI UI

Assuming you are subscribed to OpenAI Plus, all you need to do is click on 'Explore GPTs ' and then select 'Create' (or go directly to - https://chatgpt.com/gpts/editor ).

In the window that opens, you can configure the information necessary for the private GPT you are building. An even more convenient option is to use the 'Create' interface (the tab next to 'Configure') where you can simply tell GPT what you want to create, and it will configure everything for you, including generating an appropriate image for the logo.

See what’s happening here? We're using the generic GPT to create a new, customized GPT that is tailored to a specific task.

2. Generating code to fetch data from openSea platform

Now, we want to add new and relevant data from another data source. In our case, we want to fetch information from the OpenSea platform. To achieve this, we’ll need to write code that utilizes the OpenSea API.

I used Perplexity to generate Python code that interacts with the OpenSea API , referring to the documentation found at OpenSea API , and returns the data in a friendly format back to the GPT we are building.

By leveraging Flask , we can route incoming GET requests from the server to the appropriate functions, while utilizing the Codium Extension in 'Visual studio code' to automatically add documentation and tests. This approach ensures a streamlined development process, making it easy to integrate real-time data from OpenSea into our customized GPT model.

This kind of integration demonstrates how we can bring external, dynamic data into a tailored GPT solution, enhancing its usefulness for specific business needs.

3. Deploying our python code using PythonAnywhere

Now that we have Python code capable of working with the OpenSea API, we want to make it available for requests from the GPT we are building. There are many ways to achieve this, from setting up a server on AWS or using Lambda services, to the easiest option—leveraging PythonAnywhere , which allows you to run code on a dedicated server quickly and conveniently.

Although the free server has limitations in terms of CPU, storage, and runtime, these should not be an issue for our example. After a quick and free registration on the site, click on 'Open Web tab,' and then select 'Go to directory' to upload your code.

This setup enables us to easily deploy the Python code, making it accessible for the GPT model to process requests and deliver real-time data from OpenSea.

After uploading the file to the server, if everything is functioning correctly, all that's left to do is click the 'Reload' button and ensure that the API call works by making a simple request from the browser to the API we created.

This step confirms that the integration is successful and the server is properly handling the requests, allowing the GPT to access real-time data from OpenSea.

4. Connecting the new GPT to our server

So, we now have a working code that can interact with the OpenSea API and retrieve data, along with a Python server running the code, ready to handle incoming requests. The final step is to link the server with the new GPT we're building.

At the bottom of the Create GPT page (from step 1), you'll find a button labeled 'Create new action.' By clicking this, you can define a custom action that connects the GPT to our server, allowing it to send requests and retrieve the necessary data from OpenSea in real-time.

This final connection will enable our GPT model to seamlessly access and use the data retrieved from OpenSea, making it a highly functional tool tailored to your specific use case.

On the page that opens, you can configure how the GPT will interact with your server and specify which functions to call based on the need. I requested ChatGPT to generate a YAML file that defines the connection to the server (the file is available on the GitHub repo for those interested), but you can also use the example provided on the UI page and simply import the URL that you obtained from the server in the previous step.

This configuration step is essential to ensure the GPT knows how to communicate with your server effectively, allowing it to call the necessary functions to retrieve data from OpenSea as needed.

5. Testing and Deploying our GPT

Now, let's check on the right side to ensure everything is functioning correctly and that the responses being received meet our expectations and provide additional value to the user. Once satisfied, all that's left to do is click Deploy at the top and select GPT Store to allow anyone interested to use our new private ChatGPT.

Remember the long journey of tests and approvals we used to go through whenever releasing a new application to the App Store or Google Play? Well, no more! In just a few minutes, you can create a specialized GPT that adds value to users and share it in the OpenAI Store, reaching millions of users worldwide.

Go ahead and try it yourself - https://chatgpt.com/g/g-xLvZlpWXX-opensea-transaction-tracker

Conclusion

In conclusion, while private GPTs offer unparalleled flexibility, customization, and the ability to streamline business processes, they also come with significant responsibilities. Ensuring proper data handling, maintaining privacy, and safeguarding against misuse are critical. As we harness the power of these tailored AI tools, we must remain mindful of both their potential and their risks, ensuring that innovation is balanced with ethical considerations and security measures.

I'd love to hear if you've succeeded in creating your own private GPT! Feel free to share your experiences and any insights you gained during the process.