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Abstract: Retrieval-Augmented Generation (RAG) represents a paradigm shift in how AI systems access and leverage knowledge. Unlike traditional language models constrained by static training data, RAG dynamically grounds responses in real-world information, combining the fluency of generative models with the precision of retrieval systems. This chapter unpacks the architectural pillars that make RAG uniquely powerful—from vector embeddings and semantic search to the seamless integration of external knowledge with large language models (LLMs).