As an AI language model, ChatGPT work by processing and analyzing vast amounts of text data using machine learning algorithms to understand natural language and generate responses to user inputs.

Here's a brief overview of how it work:

Training Data: It was trained on a large corpus of text data, such as books, articles, and web pages. The training data was pre-processed to remove noise, such as HTML tags, punctuation, and stopwords.

Language Model Architecture: It based on a transformer architecture, which is a type of deep neural network designed for natural language processing. The transformer architecture uses a sequence-to-sequence approach that is particularly effective for tasks such as machine translation and text generation.

Tokenization: When you send a message, it first tokenize it into a sequence of tokens or subwords. This process breaks down the text into smaller units that are easier for the model to process.

Encoding: The tokenized input is then passed through an encoder, which generates a sequence of contextualized embeddings that capture the meaning and context of each token.

Decoding: The contextualized embeddings are then passed through a decoder, which generates a response based on the input and the knowledge stored in the model.

Post-Processing: The generated response is then post-processed to remove any noise or redundant information, such as repeating phrases or meaningless filler words.

Response Delivery: The final response is delivered to the user in natural language form.

It's important to note that ChatGPT responses are only as good as the data it was trained on, so there may be instances where it don't understand or correctly respond to your input. Nonetheless, it'll do it best to provide helpful and informative responses to your questions!

This blog is written with the help of ChatGPT 🫰