Retrieval-Augmented Generation Handbook: From Enterprise Knowledge to Trusted AI (English Edition) baixar o livro de graça

Retrieval-Augmented Generation Handbook: From Enterprise Knowledge to Trusted AI is a practical and comprehensive guide to designing, building, evaluating, securing, governing, and scaling enterprise Retrieval-Augmented Generation (RAG) systems.

As generative AI moves from experimentation into enterprise production, organizations face a critical challenge: connecting powerful language models with current, authoritative, secure, and governed organizational knowledge. This handbook presents RAG not simply as a retrieval technique, but as a complete enterprise AI discipline spanning knowledge engineering, retrieval, context engineering, generation, evaluation, security, governance, observability, and continuous improvement.

The book takes readers from foundational concepts through production architecture and advanced implementation patterns, covering:

• RAG fundamentals and the evolution from enterprise search to generative AI
• RAG architecture, methodology, and knowledge-readiness engineering
• Retrieval engineering, hybrid search, reranking, and optimization
• Multilingual RAG and cross-lingual knowledge systems
• Knowledge graph-based and multi-model RAG
• Responsible AI, guardrails, prompt-injection defense, privacy, and authorization
• RAG evaluation, quality engineering, observability, and governance
• Business rollout, ROI, operating models, and enterprise adoption
• Data pipelines, ingestion engineering, advanced RAG patterns, and LLMOps
• Production infrastructure, security architecture, and enterprise integration

The handbook is designed for AI architects, generative AI and machine-learning engineers, data engineers, enterprise architects, technology leaders, consultants, product managers, knowledge-management professionals, researchers, and students of generative AI.

Its central message is simple: good models are important, good retrieval is essential, good knowledge is foundational, and good governance makes AI trustworthy.

Whether you are evaluating your first RAG use case or designing an enterprise-scale knowledge-grounded AI platform, this handbook provides a structured framework for moving from prototype to reliable production system.

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