An in-depth introduction to Retrieval-Augmented Generation (RAG), explaining how it enhances Large Language Models (LLMs) by integrating external knowledge for accurate, context-aware responses. It further details the RAG pipeline using frameworks like LlamaIndex for document processing and query management, and extensively covers ChromaDB as a vector database for efficient semantic search and filtering in RAG applications