This podcast provides an extensive overview of Large Language Models (LLMs), explaining their underlying mechanisms and functionalities. It delves into backpropagation, a fundamental concept in deep learning, and discusses the attention mechanism, a key innovation that enables LLMs to capture long-range dependencies in text. The source explores LLM architectures, including the Transformer and its application in LLMs like GPT-3 and GPT-4. It outlines the process of fine-tuning LLMs for specific tasks and highlights challenges associated with their use, such as bias, computational resources, and hallucinations. The podcast further explains what is tokenization, embedding layers, and various pre-training objectives, providing a comprehensive understanding of the techniques employed in building and training LLMs.