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…mehr
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 casesMaster advanced retrieval strategies including graph-based approaches and agentic systemsOptimize performance through sophisticated chunking, embedding, and vector database techniquesNavigate the integration of RAG with modern LLMs and generative AI frameworksImplement robust evaluation frameworks and quality assurance processesDeploy scalable solutions with proper security, privacy, and governance controls Real-World Applications Intelligent document analysis and knowledge extraction
Artikelnr. des Verlages: 89514629, 979-8-8688-1807-3
First Edition
Seitenzahl: 414
Erscheinungstermin: 4. Dezember 2025
Englisch
Abmessung: 235mm x 155mm
ISBN-13: 9798868818073
Artikelnr.: 74758567
Herstellerkennzeichnung
Libri GmbH
Europaallee 1
36244 Bad Hersfeld
gpsr@libri.de
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 (RAG).- Chapter 3: Building a Retrieval-Augmented Generation (RAG) Application.- Part II: Core Components.- Chapter 4: Document Loaders: The Gateway to Knowledge.- Chapter 5: Text Splitters in RAG Systems.- Chapter 6: Embedding Models: Converting Text to Vectors.- Chapter 7: Vector Stores: Organizing and Retrieving Your Knwledge.- Chapter 8: Retrievers: Finding the Most Relevant Information.- Part III: Advanced Implementation.- Chapter 9: Prompt Templates: The Communication Experts that Structure Interactions with the LLM.- Chapter 10: RAG in Action: Advanced Patterns for Unstructured Data.- Chapter 11: RAG for Structured Data: Building Question-Answering Systems for SQL Databases and CSV Files.- Chapter 12: Graph RAG: Leveraging Knowledge Graphs for Enhanced Retrieval.- Chapter 13: Agentic RAG: Building Autonomous Information Systems.- Part IV: Production and Evaluation.- Chapter 14: RAG Evaluation: Measuring Quality and Performance.
Part I: Foundations.- Chapter 1: Introduction to Retrieval-Augmented Generation (RAG).- Chapter 2: Core Concepts of Retrieval-Augmented Generation (RAG).- Chapter 3: Building a Retrieval-Augmented Generation (RAG) Application.- Part II: Core Components.- Chapter 4: Document Loaders: The Gateway to Knowledge.- Chapter 5: Text Splitters in RAG Systems.- Chapter 6: Embedding Models: Converting Text to Vectors.- Chapter 7: Vector Stores: Organizing and Retrieving Your Knwledge.- Chapter 8: Retrievers: Finding the Most Relevant Information.- Part III: Advanced Implementation.- Chapter 9: Prompt Templates: The Communication Experts that Structure Interactions with the LLM.- Chapter 10: RAG in Action: Advanced Patterns for Unstructured Data.- Chapter 11: RAG for Structured Data: Building Question-Answering Systems for SQL Databases and CSV Files.- Chapter 12: Graph RAG: Leveraging Knowledge Graphs for Enhanced Retrieval.- Chapter 13: Agentic RAG: Building Autonomous Information Systems.- Part IV: Production and Evaluation.- Chapter 14: RAG Evaluation: Measuring Quality and Performance.
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