This video provides a comprehensive deep dive into Large Language Models (LLMs) like ChatGPT, explaining their entire training pipeline from pre-training on vast internet data to supervised fine-tuning and advanced reinforcement learning techniques. It explores the underlying neural network architecture, tokenization, and the computational demands of training, while also discussing the cognitive 'psychology' of LLMs, including hallucinations, emergent reasoning capabilities, and practical limitations. The presentation concludes with insights into future multimodal capabilities, agentic behavior, and practical advice for using LLMs effectively as powerful, yet fallible, tools.