9798249845575 - rag faq: simple answers to common questions on retrieval-augmented generation di antonimuthu, rajamanickam (9 risultati)
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Paperback. Condizione: new. Paperback. Unlock the full potential of Retrieval-Augmented Generation (RAG) with RAG FAQ, your practical guide to understanding and applying this cutting-edge AI paradigm. Whether you're a developer, data scientist, or AI enthusiast, this book delivers clear, concise answers to 50 of the most common…and advanced questions about RAG-all in one place.Inside, you'll discover: How RAG works and why retrieval is critical for large language modelsKey concepts like embeddings, vector databases, hybrid search, and rerankingAdvanced strategies including Self-RAG, Corrective RAG, and agentic pipelinesMultimodal RAG for combining text, images, audio, and structured dataOptimization techniques for latency, cost, and answer qualitySecurity, access control, and citation best practicesEmerging trends and future directions in RAGThis book doesn't just explain the theory-it gives you actionable engineering insights and real-world examples to build robust, production-ready RAG systems.Why this book is for you: Learn RAG without jargon or hypeMake smarter decisions for retrieval, embeddings, and vector databasesAvoid common mistakes and anti-patternsStay up-to-date with the latest trends in AI-powered knowledge retrievalPerfect for AI practitioners, researchers, and anyone looking to leverage RAG to build smarter, more reliable AI applications. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Paperback. Condizione: new. Paperback. Unlock the full potential of Retrieval-Augmented Generation (RAG) with RAG FAQ, your practical guide to understanding and applying this cutting-edge AI paradigm. Whether you're a developer, data scientist, or AI enthusiast, this book delivers clear, concise answers to 50 of the most common…and advanced questions about RAG-all in one place.Inside, you'll discover: How RAG works and why retrieval is critical for large language modelsKey concepts like embeddings, vector databases, hybrid search, and rerankingAdvanced strategies including Self-RAG, Corrective RAG, and agentic pipelinesMultimodal RAG for combining text, images, audio, and structured dataOptimization techniques for latency, cost, and answer qualitySecurity, access control, and citation best practicesEmerging trends and future directions in RAGThis book doesn't just explain the theory-it gives you actionable engineering insights and real-world examples to build robust, production-ready RAG systems.Why this book is for you: Learn RAG without jargon or hypeMake smarter decisions for retrieval, embeddings, and vector databasesAvoid common mistakes and anti-patternsStay up-to-date with the latest trends in AI-powered knowledge retrievalPerfect for AI practitioners, researchers, and anyone looking to leverage RAG to build smarter, more reliable AI applications. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
