In this video, Sam Witteveen discusses the four stacks of LLM (Large Language Model) apps and agents, providing a framework for building and understanding these applications. The four stacks are: 1) LLM Stack, 2) Search/Memory/Data Stack, 3) Reasoning and Action Stack, and 4) Personalization Stack. The LLM Stack includes elements related to the large language model itself, such as pre-training, fine-tuning, and serving the model. The Search/Memory/Data Stack involves obtaining and injecting information into the model, including semantic search, vector stores, and memory systems. The Reasoning and Action Stack focuses on decision-making and executing actions, often using solvers and reasoning engines. The Personalization Stack deals with prompt engineering, creating a personality for the model, and customizing interactions. Sam explains how to think about each stack when designing LLM apps or agents, highlighting the importance of understanding the role of each component. He also provides examples of tools and methods for implementing these stacks, such as using Llama Index for data-related tasks. The video aims to help viewers conceptualize the architecture of LLM applications and offers insights into the various elements involved in their development.