Building Intelligent Systems with LLMs: RAG and Agents

The episode extensively explores Retrieval-Augmented Generation (RAG), a technique for enhancing large language models with external data to improve accuracy and reduce hallucinations. It details the implementation of RAG using frameworks like LangChain and LlamaIndex, covering aspects such as data loading, indexing, retrieval strategies, and query engines. Furthermore, the document discusses agent development, showcasing how LLMs can be used as reasoning engines with tools for complex tasks, and introduces concepts like AutoGPT and communicative agents. Finally, it examines evaluation metrics for RAG systems and the use of platforms like LangSmith and the OpenAI Assistants API in building advanced AI applications