This episode explores advanced techniques for improving large language model performance beyond basic prompting, focusing on two main patterns: Retrieval-Augmented Generation (RAG) and agents. It explains how RAG enhances responses by retrieving relevant information from external sources, detailing different retrieval methods like term-based and embedding-based approaches, and strategies for optimization. The text then introduces agents, which utilize tools and planning to accomplish complex tasks, discussing various types of tools and planning techniques, including reflection and error correction. Finally, it highlights the importance of memory systems for RAG and agents to manage and retain information across queries and sessions.