Welcome! Today we'll explore how ChatGPT actually works. Unlike search engines that look through databases, ChatGPT generates responses using knowledge embedded in its neural network. This knowledge comes from training on billions of text examples from books, websites, and other sources.
The training process is where the magic happens. ChatGPT learns from billions of text examples during training. It analyzes patterns in language, understands context, and builds connections between concepts. This knowledge gets encoded into the neural network's parameters, creating a compressed representation of human knowledge.
Now let's see how ChatGPT generates responses. When you ask a question, the model processes your input through its neural network. It doesn't search through stored text. Instead, it uses the patterns it learned to predict the most probable next words, building a response token by token until it forms a complete, coherent answer.
The key difference is fundamental. Search engines look through indexed documents and return matching results from databases. They retrieve existing information. ChatGPT, however, generates entirely new text based on patterns it learned during training. It creates responses rather than finding them, using its neural network to predict the most appropriate words for each context.
To summarize: ChatGPT doesn't search through billions of data points like a search engine. Instead, its knowledge is compressed and embedded within the neural network's parameters during training. When you ask a question, it processes your input and generates a response by predicting the most appropriate words based on the patterns it learned. This makes ChatGPT a generative AI that creates new text rather than retrieving existing information.