Building a custom ChatGPT with a personalised knowledge base is achievable through fine-tuning the GPT-3 model. This process involves leveraging the model’s ability to generate human-like text, while enhancing its knowledge base with specific information. The first step requires obtaining the GPT-3 API key from OpenAI, followed by installing Python and the OpenAI Python client.

Fine-tuning the model involves two steps: pre-training and fine-tuning. Pre-training is the initial phase where the model learns to predict the next word in a sentence. Fine-tuning, on the other hand, is where the model is trained on a specific task, using a custom dataset.

The article guides readers on how to create a custom dataset, which includes the chat history and system level instructions. The system level instructions are crucial to guide the model’s response, while the chat history helps the model understand the context of the conversation.

The fine-tuning process is concluded by testing the model and iteratively improving it. The end product is a ChatGPT model with a tailored knowledge base, capable of generating more accurate and context-specific responses.

Go to source article: https://betterprogramming.pub/how-to-build-your-own-custom-chatgpt-with-custom-knowledge-base-4e61ad82427e