EP 35 | CME295: LLM Reasoning

Can a language model actually think, or is it just a sophisticated autocomplete? In this episode, we dive into the fascinating—and highly debated—topic of LLM reasoning. We explore how to unlock complex problem-solving capabilities without changing the underlying model.Key Topics:In-Context Learning: How models can learn to do new tasks on the fly just from the examples provided in your prompt.Chain-of-Thought (CoT): Why forcing a model to "show its work" step-by-step drastically improves its ability to solve math and logic puzzles.The Illusion of Understanding: The boundaries of current AI reasoning, tackling why models hallucinate facts and where their logic ultimately breaks down.Note: This is an AI-generated study resource created via NotebookLM based on the Stanford CME295 curriculum and personal study notes.