how chatgpt works

ChatGPT is a variant of OpenAI’s Generative Pre-trained Transformer 3 (GPT-3) model. It is trained on a large corpus of text data to generate human-like text responses to questions and prompts.

Here’s how it works:

1. Input: The user provides a prompt or a question in natural language text.
2. Preprocessing: The input text is preprocessed to convert it into a machine-readable format and feed it into the model.
3. Tokenization: The input text is tokenized, i.e., split into individual words or subwords, and each token is assigned a unique numerical identifier, called a token ID.
4. Embedding: The token IDs are then passed through an embedding layer to convert them into dense vectors, also known as embeddings, that represent the input text in a continuous vector space.
5. Encoder: The embeddings are then passed through a series of Transformer encoder blocks, which are comprised of multi-head attention layers, feedforward layers, and residual connections. The encoder generates a compact representation, or context, of the input text.
6. Decoder: The context is then fed into a Transformer decoder, which generates the response. The decoder has access to the entire context and uses it to generate the output text one token at a time, using a technique called auto-regressive generation.
7. Output: The decoder outputs the token IDs, which are then converted back into text using a vocabulary.

The model is trained using a large corpus of text data and a variant of the maximum likelihood estimation (MLE) objective, which aims to maximize the probability of the model generating the target text given the input text.

During inference, the model uses the context generated from the input text to generate the response, making use of its training to generate human-like text that is relevant and coherent in context.