Delivered at Microsoft Build 2023, this talk by Andrej Karpathy lays out the full pipeline used to turn a raw language model into a helpful assistant like ChatGPT. He breaks the process into four stages: pretraining on a large text corpus, supervised finetuning on curated examples, reward modeling, and reinforcement learning from human feedback. For each stage he explains what data is used, what the model is learning, and what the stage costs in time and compute.
The second half of the talk shifts to how to use these models well. Karpathy discusses prompting strategies, why chain-of-thought style prompts help, the role of tools and retrieval, and the practical limits of what current assistants can and cannot do reliably. He frames the model as a token simulator that benefits from being given room to think.
This is one of the most efficient ways to understand modern LLM assistants in a single sitting. Karpathy was a founding member of OpenAI and led AI at Tesla, and he distills a complicated, fast-moving field into a coherent mental model. For a business or technical reader who wants to grasp both how these systems are built and how to get good results from them, the talk is a high-value starting point.