A large language model functions by predicting the next word in a text sequence based on prior context. It does this by assigning probabilities to all possible next words rather than providing a single definite answer. To create a chatbot, the interaction between a user and an AI assistant is structured, with the user's input guiding the model's word predictions. This process allows for varied and natural responses, as the model can include less likely word choices randomly, leading to different outputs each run. The training of these models involves analyzing vast amounts of text, with the training data being equivalent to what a person would take over 2600 years to read. Training involves adjusting a multitude of parameters or weights, which fundamentally determine the model's behavior and word prediction capabilities. These parameters begin randomly, resulting in nonsensical initial outputs, but are refined through continuous learning from examples. Training utilizes backpropagation to adjust parameters based on the comparison between predicted and actual final words in the training examples, ensuring that the model improves its predictive accuracy over time. The term large in large language models refers to the immense number of these parameters, sometimes reaching hundreds of billions, which significantly enhances their language understanding and generation capabilities.