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“Retrieval-Augmented Generation” (RAG) as a book category covers how to combine information retrieval with large language models to produce grounded, up-to-date, and verifiable outputs. Books in this genre explain the theory and practice of RAG systems, including embeddings and vector search, chunking and indexing, keyword/dense/hybrid retrieval, reranking, multi-hop retrieval, and context selection and prompting. They detail architectures and tooling (vector databases, LLM frameworks, pipelines), evaluation and guardrails (hallucination reduction, relevance and faithfulness metrics, RAGAS), and trade-offs in latency, cost, privacy, and quality. Expect practical implementation guides (e.g., with LangChain, LlamaIndex, and cloud services), case studies across domains like enterprise knowledge, customer support, search, legal, and healthcare, and comparisons to alternatives such as fine-tuning. The category spans primers, practitioner handbooks, and research-oriented texts aimed at engineers, data scientists, and product teams building reliable AI applications.

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