This podcast provides a detailed explanation of how large language models (LLMs) like ChatGPT are built and function. The speaker walks the listener through the three main stages of LLM training: pre-training (using internet data), supervised fine-tuning (creating conversational datasets), and reinforcement learning (refining responses through trial and error). The discussion highlights the importance of tokenization, the challenges of hallucinations, and the use of tools like web search and code interpreters to improve accuracy. A key takeaway is that LLMs are powerful tools but should not be treated as infallible; users should always verify their output. The podcast concludes by discussing future LLM capabilities, including multimodality and more sophisticated agentic behavior.
