This beginner-friendly path introduces the core ideas behind Retrieval-Augmented Generation, a common approach for improving language model responses by grounding them in retrieved information. You will compare basic prompting with RAG workflows and learn why retrieval can make generated output more accurate and context-aware. The path covers practical building blocks for RAG systems, including text representation techniques, embeddings, semantic search, document chunking, and vector database storage. You will also explore how to retrieve relevant information, construct prompts from retrieved context, and manage document updates at scale. As you progress, you will examine ways to improve RAG pipelines through hybrid retrieval, query refinement, context summarization, and constraints that keep responses tied to retrieved sources. This path is designed for learners who want a clear starting point for building and evaluating RAG-based applications.
