ChatGPT is designed to generate coherent and contextually relevant responses by using a technique called "transformer architecture" that allows it to model long-range dependencies and capture the context of the conversation. When ChatGPT receives a user input, it first tries to understand the meaning and intent behind it. It then uses this information, along with the context of the ongoing conversation and its previous training data, to generate a response that is relevant and coherent.
Additionally, ChatGPT has been trained on a massive corpus of text data, which enables it to generate responses that are consistent with the patterns and structures of natural language. It has also been fine-tuned on specific domains and topics, which further improves its ability to generate relevant and coherent responses in those areas. Finally, ChatGPT has the ability to learn from its interactions with users, which enables it to improve its responses over time and adapt to different conversational styles and contexts.