Journey into the fascinating world of artificial intelligence as we explore Andrej Karpathy's illuminating breakdown of how large language models like ChatGPT are created from the ground up.

This episode unveils the three-stage process that transforms raw internet text into sophisticated AI assistants capable of human-like conversation. We begin with pre-training, where models digest vast oceans of text from across the internet, learning patterns and relationships between words, concepts, and ideas without any specific goal beyond prediction.

Next, we examine the critical post-training phase, where these raw models are refined into helpful assistants through supervised fine-tuning. Discover how human labelers craft ideal responses to thousands of queries and how, in a surprising twist, AI systems are increasingly generating and editing their own training data—AI teaching AI.

Perhaps most fascinating is our exploration of reinforcement learning, where models develop problem-solving strategies through trial and error, leading to emergent cognitive behaviors that weren't explicitly programmed. This process reveals how modern AI systems develop capabilities that sometimes surprise even their creators.

We conclude with an important discussion about the practical limitations of these systems, including their tendency to hallucinate information and the importance of using them as tools rather than oracles. Whether you're an AI enthusiast or simply curious about the technology reshaping our digital landscape, this episode offers a clear window into how modern AI assistants are built, trained, and refined.