- Training: ChatGPT is trained on an extensive range of text data from the internet, allowing it to understand and generate human-like language. This training involves exposing the model to diverse contexts, grammar, and idiomatic expressions.
- Tokenization: The AI processes text by breaking it down into tokens, which are smaller units like words or subwords. This helps the model manage and generate text efficiently.
- Transformer Architecture: At the core of ChatGPT is the Transformer, a neural network that excels in handling sequential data. It uses layers of attention mechanisms to weigh the importance of different words in a sentence, allowing it to understand context and nuances.
- Contextual Understanding: ChatGPT maintains context through interactions, enabling it to provide coherent and relevant responses over extended conversations. This is achieved by considering previous parts of the conversation to inform its replies.
- Generative Abilities: By predicting the next word in a sequence, ChatGPT can generate text that is fluid and contextually appropriate. This makes it versatile for tasks like drafting emails, answering queries, and creating content.
