Custom cover image
Custom cover image

Mastering retrieval-augmented generation : advanced techniques and production-ready solutions for Enterprise AI / Ranajoy Bose

By: Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Publisher: New York, NY ; [Berkeley, CA] : Apress, [2025]Copyright date: © 2025Description: 1 Online-Ressource (xxxix, 820 Seiten) : IllustrationenISBN:
  • 9798868818080
Subject(s): Additional physical formats: 9798868818073 | 9798868818097 | Erscheint auch als: 9798868818073 Druck-Ausgabe | Erscheint auch als: 9798868818097 Druck-AusgabeDDC classification:
  • 006.3 23
DOI: DOI: 10.1007/979-8-8688-1808-0Online resources: Summary: 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.Summary: 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 enterprise sectors and use cases.PPN: PPN: 1947801791Package identifier: Produktsigel: ZDB-2-SEB | ZDB-2-CWD | ZDB-2-SXPC
No physical items for this record

Barrierefreier Inhalt: PDF/UA-1. Table of contents navigation. Single logical reading order. Short alternative textual descriptions. Use of color is not sole means of conveying information. Use of high contrast between text and background color. Next / Previous structural navigation. All non-decorative content supports reading without sight

Anmerkungen zur Barrierefreiheit: This PDF has been created in accordance with the PDF/UA-1 standard to enhance accessibility, including screen reader support, described non-text content (images, graphs), bookmarks for easy navigation, keyboard-friendly links and forms and searchable, selectable text. We recognize the importance of accessibility, and we welcome queries about accessibility for any of our products. If you have a question or an access need, please get in touch with us at accessibilitysupport@springernature.com. Please note that a more accessible version of this eBook is available as ePub.. No reading system accessibility options actively disabled. Publisher contact for further accessibility information: accessibilitysupport@springernature.com

KIT – The University in the Helmholtz Association                         Home | Impressum | Datenschutz | Barrierefreiheit | KIT