ChatGPT works as follows:
It takes in a prompt or a question as input, represented as a sequence of tokens (words or sub-words).
The input sequence is fed into an encoder layer, which converts the input tokens into hidden states.
The hidden states are then passed through multiple transformer blocks, which use self-attention mechanisms to allow the model to focus on different parts of the input sequence.
After processing the input sequence, the decoder generates the output sequence, one token at a time.
During each generation step, the decoder takes in the previous hidden states, the previous token, and the attention-weighted sum of the encoder hidden states, and produces the next hidden state and the next token to generate.
The process continues until the model predicts an end-of-sequence token or a maximum length is reached.
The output sequence is then converted back into a readable form, such as text.
Overall, ChatGPT uses a deep neural network with a large number of parameters to generate a response based on the input prompt or question.