Artificial Intelligence (AI) has made impressive strides in recent years, particularly in the field of Natural Language Processing (NLP). One of the standout products of these developments is the Generative Pre-trained Transformer (GPT) model by OpenAI. This expert article explains the process and illustrates how specialized AI assistants, for example, for use in a company's marketing, can be designed and implemented.
Step 1: Understanding the Basics and Getting Started
Before creating a custom GPT, it is important to gain a basic understanding of how it works and the underlying technology. GPTs are models pre-trained on large datasets with the ability to generate or understand text in a manner similar to human communication. To get started, all you need is a ChatGPT Plus subscription ($20 per month). Click on "Explore GPTs" and then "Create GPT" on chat.openai.com. Then, you can begin. A new GPT is "programmed" with the help of a chatbot that guides the user step by step through the process.
Step 2: Selection and Preparation of Data
The performance of a specific GPT model naturally depends on the quality and relevance of the data it is trained on. For creating a company marketing assistant, for example, data from the website can be used. These are extensive, reflect the corporate identity, and since they are publicly accessible anyway, they should not pose a security risk regarding storage with OpenAI. Nevertheless, they provide the model with a lot of "material" to correctly learn the products, history, other company information, and especially the tone of the language. But other data like company presentations, business reports, etc., could also be used.
The selected data form the basis for further use. However, it is important to consider the mentioned risk of data storage.
Step 3: Training and Fine-tuning
After the data preparation, the model is trained on the OpenAI platform. This process involves fine-tuning a pre-trained GPT model with the specifically selected data. In this step, ChatGPT asks the user specific questions. First, it wants to know what it should be able to do, what its tasks are, and what data it should work with. Then follows the fine-tuning. It asks, for example, what it should emphasize or avoid, which topics, areas, etc., are particularly important, and also what the tone should be. During the training process, it is important to continuously monitor the model's performance and make adjustments if necessary to achieve the desired accuracy and efficiency. There is a chat window next to the training window on the left side, where you can continually test the new GPT until you are satisfied.
Step 4: Deployment and Usage
In the final step, you save and decide whether the GPT is private or public, and it's done. For marketing employees directly on the OpenAI platform, the custom GPT is now ready for use.
If you want to take it a step further, once a model has been successfully trained and optimized, it can also be integrated into a desired environment such as a website, so it is available to all users (not for free). This would make sense if you developed the custom GPT, for example, to help customers navigate the website better. This approach involves technical steps like setting up APIs, security measures, and user interfaces to ensure a seamless and user-friendly experience.
Conclusion
Creating a custom GPT on the OpenAI.com platform does require an understanding of the technology as well as careful preparation and selection of data, but at least up to step 3, it does not require any programming skills. This makes custom GPTs a simple yet very effective tool. However, data protection is very important. It is advisable to avoid using internal company data in custom GPTs. It is better to specialize the model on a specific task using public data. By following the described steps, anyone can create a specialized GPT model tailored to individual needs and requirements. And there are hardly any limits to creativity.
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This article was written as part of the CAS Digital Product Lead at the Institute for Digital Business of the «Hochschule für Wirtschaft Zürich».
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