Mastering RAG Fundamentals and Advanced Implementation Techniques

This video covers Retrieval-Augmented Generation (RAG), from its fundamental architecture to common challenges and advanced optimisation techniques. It explains how naive RAG connects internal documents to large language models, and how advanced strategies improve retrieval quality. In this video you will learn: How naive RAG works: chunking documents, creating vector embeddings, and searching a vector database such as ChromaDB The limits of naive RAG, including weak contextual understanding, poor component integration, and scaling problems Advanced query expansion using hypothetical answers and multiple subqueries Hands-on Python builds with LangChain and OpenAI for document-chat systems Source: NotebookLM — AI Research with Anupama / New AI/ML research video every day #AI #MachineLearning #DeepLearning