Advanced Graph RAG Mastery : Foundations and Production Systems: A Practical Guide to Developing Graph RAG Foundations for Production-Ready AI Systems and LLM Integration

Forge Silas

ISBN 13: 9798191091358

Edité par Amazon Digital Services LLC - Kdp Aug 2026, 2026

A propos de cet article

Neuware - What if your AI system could truly understand context instead of just retrieving fragments Most Retrieval-Augmented Generation (RAG) systems rely on vector search. They retrieve relevant chunks, but they lack structure, relationships, and deep reasoning. The result is familiar: hallucinations, shallow answers, and inconsistent outputs.The future of AI retrieval is not flat. It is connected.Advanced Graph RAG Mastery: Foundations and Production Systems is a comprehensive, hands-on guide to building the next generation of intelligent AI systems using Graph RAG. This book goes beyond traditional approaches and shows you how to design systems that understand relationships, reason across multiple sources, and deliver accurate, explainable results.Written for developers, AI engineers, and technical practitioners, this book bridges the gap between foundational theory and real-world implementation. You will not only understand how Graph RAG works, but also how to build, optimize, and deploy it in production environments.Inside this book, you will learn how to: - Move beyond vector-only retrieval and design graph-based retrieval architectures- Extract entities and relationships using large language models- Build and maintain scalable knowledge graphs from unstructured data- Implement advanced retrieval strategies including multi-hop and hybrid search- Design systems that reduce hallucination and improve factual accuracy- Integrate Graph RAG pipelines with modern LLM frameworks- Evaluate, optimize, and productionize AI systems for real-world useThis is not a theoretical overview. Every concept is grounded in practical engineering, with clear explanations and real implementation patterns that you can apply immediately.Whether you are building enterprise AI systems, research tools, or intelligent applications, this book gives you the tools to create systems that are not only powerful, but also reliable and explainable.If you are ready to move beyond basic RAG and master the architecture shaping the future of AI, this book will guide you every step of the way.The next evolution of retrieval is here.Build it.

N° de réf. du vendeur 9798191091358

Détails bibliographiques

• Titre: Advanced Graph RAG Mastery : Foundations and Production Systems: A Practical Guide to Developing Graph RAG Foundations for Production-Ready AI Systems and LLM Integration
• Éditeur: Amazon Digital Services LLC - Kdp Aug 2026
• Date de parution: 2026
• Langue: anglais
• ISBN à 13 chiffres: 9798191091358
• Reliure: Taschenbuch
• État de l'article: Neu
• Dimensions: 254 L x 178 H x 7 P (en millimètres) 254 millimètres de largeur par 178 millimètres de hauteur par 7 millimètres de profondeur
• Poids: 240 grammes

A propos de ce titre

Synopsis

What if your AI system could truly understand context instead of just retrieving fragments?

Most Retrieval-Augmented Generation (RAG) systems rely on vector search. They retrieve relevant chunks, but they lack structure, relationships, and deep reasoning. The result is familiar: hallucinations, shallow answers, and inconsistent outputs.

The future of AI retrieval is not flat. It is connected.

Advanced Graph RAG Mastery: Foundations and Production Systems is a comprehensive, hands-on guide to building the next generation of intelligent AI systems using Graph RAG. This book goes beyond traditional approaches and shows you how to design systems that understand relationships, reason across multiple sources, and deliver accurate, explainable results.

Written for developers, AI engineers, and technical practitioners, this book bridges the gap between foundational theory and real-world implementation. You will not only understand how Graph RAG works, but also how to build, optimize, and deploy it in production environments.

Inside this book, you will learn how to:

1. Move beyond vector-only retrieval and design graph-based retrieval architectures

2. Extract entities and relationships using large language models

3. Build and maintain scalable knowledge graphs from unstructured data

4. Implement advanced retrieval strategies including multi-hop and hybrid search

5. Design systems that reduce hallucination and improve factual accuracy

6. Integrate Graph RAG pipelines with modern LLM frameworks

7. Evaluate, optimize, and productionize AI systems for real-world use

This is not a theoretical overview. Every concept is grounded in practical engineering, with clear explanations and real implementation patterns that you can apply immediately.

Whether you are building enterprise AI systems, research tools, or intelligent applications, this book gives you the tools to create systems that are not only powerful, but also reliable and explainable.

If you are ready to move beyond basic RAG and master the architecture shaping the future of AI, this book will guide you every step of the way.

The next evolution of retrieval is here.

Build it.