Firstly, the GPT models are trained on vast amounts of text data from the internet, making them capable of generating text in a wide range of styles and genres. This training data is often referred to as "corpus", and it is used to teach the model how to recognize and generate patterns in language. Secondly, the GPT models are built using a neural network architecture known as the transformer, which allows the model to process and generate text in a more efficient and effective way than previous approaches. The transformer architecture enables the model to learn long-range dependencies and relationships between words and phrases, allowing it to generate more coherent and contextually relevant text. Lastly, the GPT models are designed to be highly scalable and flexible, allowing them to be fine-tuned on specific tasks or domains with relative ease. This makes them a powerful tool for a wide range of natural language processing applications, from language translation and content creation to chatbots and virtual assistants.