Ranajoy Bose

Mastering Retrieval-Augmented Generation

Advanced Techniques and Production-Ready Solutions for Enterprise AI

• Broschiertes Buch

Produktbeschreibung

Retrieval-Augmented Generation (RAG) represents the cutting edge of AI innovation, bridging the gap between large language models (LLMs) and real-world knowledge. This book provides the definitive roadmap for building, optimizing, and deploying enterprise-grade RAG systems that deliver measurable business value. This comprehensive guide takes you beyond basic concepts to advanced implementation strategies, covering everything from architectural patterns to production deployment. You'll explore proven techniques for document processing, vector optimization, retrieval enhancement, and system scaling, supported by real-world case studies from leading organizations. Key Learning Objectives * 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 Real-World Applications * 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 sectors, including finance, healthcare, legal, and technology

Produktdetails

• Verlag: APRESS L.P. / Apress L.P.
• Artikelnr. des Verlages: 89514629
• First Edition
• Erscheinungstermin: 26. Dezember 2025
• Englisch
• Abmessung: 235mm x 155mm
• ISBN-13: 9798868818073
• Artikelnr.: 74758567

Autorenporträt

Ranajoy Bose is a technologist, entrepreneur, and thought leader in the fields of Generative AI, MLOps, and enterprise data systems. As Co-founder and Global Head of Engineering at Morfius, he is at the helm of building cutting-edge AI solutions that power real-world transformation through Retrieval-Augmented Generation (RAG) and large-scale language models. Before Morfius, Ranajoy held leadership roles at Oracle, where he led the Cloud Engineering organization for North America. His work was instrumental in advancing the adoption of data lakehouse architectures, modern analytics, AI/ML platforms, and cloud-native services for Fortune 500 clients. Recognized as a 40-under-40 Data Scientist, Ranajoy also led a team ranked among Analytics India Magazine’s Top 10 data science workplaces. Beyond his corporate leadership, he remains a committed advocate for innovation and learning—frequently speaking at global conferences, contributing to academic and industry forums, and mentoring the next generation of AI practitioners. Driven by curiosity and purpose, Ranajoy continues to push the boundaries of enterprise AI, translating complex technology into impactful solutions for the modern world.

Inhaltsangabe

Part I: Foundations.
Chapter 1: Introduction to Retrieval
Augmented Generation (RAG).
Chapter 2: Core Concepts of Retrieval
Augmented Generation.
Chapter 3: Build a Retrieval
Augmented Generation (RAG) Application.
Part II: Core Components.
Chapter 4: Document Loaders and Data Ingestion Strategies.
Chapter 5: Text Splitting and Chunking Optimization.
Chapter 6: Embedding Models: From Text to High
Dimensional Vectors.
Chapter 7: Vector Databases: Storage and Retrieval at Scale.
Chapter 8: Advanced Retrieval Strategies and Optimization.
Part III: Advanced Implementation.
Chapter 9: Prompt Engineering and Template Design for RAG.
Chapter 10: Advanced RAG Patterns for Unstructured Data.
Chapter 11: RAG for Structured Data: SQL, CSV, and Tabular Intelligence.
Chapter 12: Graph RAG: Knowledge Graphs for Enhanced Context.
Chapter 13: Agentic RAG: Building Autonomous Information Systems.
Part IV: Production and Evaluation.
Chapter 14: RAG Evaluation: Metrics, Benchmarks, and Quality Assurance.
Chapter 15: Production Deployment and Scaling Strategies.
Chapter 16: Security, Privacy, and Ethical Considerations in Enterprise RAG.