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Mastering Retrieval-Augmented Generation: Advanced Techniques and Production-Ready Solutions for Enterprise AI
Paperback
General ComputersProbability & Statistics
Publisher: Apress
Published: Jan 3 2026
Pages: 820
Weight: 2.61
Height: 1.71 Width: 6.14 Depth: 9.21
Language: English
- Design and implement production-ready RAG architectures for diverse enterprise use cases Master advanced retrieval strategies including graph-based approaches and agentic systems Optimize performance through sophisticated chunking, embedding, and vector database techniques Navigate the integration of RAG with modern LLMs and generative AI frameworks Implement robust evaluation frameworks and quality assurance processes Deploy scalable solutions with proper security, privacy, and governance controls
- Intelligent document analysis and knowledge extraction Code generation and technical documentation systems Customer support automation and decision support tools Regulatory compliance and risk management solutions
Whether you're an AI engineer scaling existing systems or a technical leader planning next-generation capabilities, this book provides the expertise needed to succeed in the rapidly evolving landscape of enterprise AI.
What You Will Learn
- Architecture Mastery: Design scalable RAG systems from prototype to enterprise production Advanced Retrieval: Implement sophisticated strategies, including graph-based and multi-modal approaches Performance Optimization: Fine-tune embedding models, vector databases, and retrieval algorithms for maximum efficiency LLM Integration: Seamlessly combine RAG with state-of-the-art language models and generative AI frameworks Production Excellence: Deploy robust systems with monitoring, evaluation, and continuous improvement processes Industry Applications: Apply RAG solutions across diverse enterprise sectors and use cases
Who This Book Is For
Primary audience: Senior AI/ML engineers, data scientists, and technical architects building production AI systems; secondary audience: Engineering managers, technical leads, and AI researchers working with large-scale language models and information retrieval systems Prerequisites: Intermediate Python programming, basic understanding of machine learning concepts, and familiarity with natural language processing fundamentals
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