Large Language Models, or LLMs, are instances of foundation models that utilize large amounts of unlabeled and self-supervised data to generate human-like text. They are specifically designed for handling text and text-like data, including code, and are trained on extensive datasets comprising books, articles, and conversations. The scale of these models is significant, often involving tens of gigabytes in size and potentially trained on petabytes of data. A single gigabyte can contain around 178 million words, while a petabyte consists of about 1 million gigabytes. One prominent example is GPT-3, which is pre-trained on 45 terabytes of data and utilizes 175 billion machine learning parameters. LLMs operate based on three primary components: data, architecture, and training. The architecture typically involves neural networks, specifically transformers, which allow the model to process sequences of data effectively. Transformers analyze words in context, leading to a comprehensive understanding of sentence structure. During training, the model learns to predict the next word in a sentence, starting with random guesses and iteratively adjusting its parameters to enhance accuracy. This complex interplay of data, architecture, and training enables LLMs to produce coherent and contextually relevant text responses, making them versatile tools for various applications.