Chapter 4 examines Retrieval-Augmented Generation (RAG) as a leading framework to enhance the factual reliability and knowledge grounding of large language models. RAG integrates semantic retrieval with generative modeling, reducing hallucinations and enabling real-time citation, traceability, and multi-source synthesis. The chapter details RAG’s technical architecture, including data cleansing, embedding pipelines, and vector databases, and explores advanced applications in enterprise, law, finance, and deep research scenarios. It discusses challenges such as document parsing, vector retrieval limits, and collaborative optimization, while highlighting the transformative impact of RAG on language services, knowledge management, and decision support.