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Retrieval-Augmented Generation with Knowledge Graphs: Build Intelligent, Explainable and Scalable AI Systems
Retrieval-Augmented Generation with Knowledge Build Intelligent, Explainable, and Scalable AI Systems
Unlock the next evolution of AI development by combining the power of Large Language Models (LLMs) with the structured intelligence of Knowledge Graphs. This comprehensive guide equips you with the theory, tools, and step-by-step workflows to build real-world Retrieval-Augmented Generation (RAG) systems—enhanced with graph-based reasoning, transparency, and scalability.
Whether you're a machine learning engineer, data scientist, AI researcher, or enterprise architect, this book offers a practical roadmap to mastering KG-RAG systems, from foundational concepts to full deployment.
What You’ll Learn
• Core Concepts Made Clear: Understand RAG, Knowledge Graphs, ontologies, embeddings, vector search, and graph-based retrieval.
• Hands-On Projects: Build a working KG-RAG chatbot from scratch using tools like LangChain, Neo4j, Qdrant, and GPT.
• Design Smarter AI Systems: Explore graph-enhanced prompt engineering, semantic retrieval, and modular pipeline architecture.
• Explainability & Trus Learn how to visualize graph paths, track provenance, and ensure transparency in AI outputs.
• Deployment at Scale: Discover best practices for cloud deployment, API design, CI/CD, and system monitoring.
• Security & Ethics: Address risks such as hallucinations, data drift, privacy, and knowledge decay.
Who Should Read This Book?
• AI/ML Engineers building next-gen applications with LLMs
• Data Scientists and NLP Specialists working on retrieval systems
• Knowledge Engineers and Ontology Designers
• CTOs, Product Managers, and Innovation Leads in AI-driven teams
• Researchers exploring hybrid neuro-symbolic architectures
What Makes This Book Unique?
Unlike generic books on LLMs or RAG, this guide goes deep into the integration of Knowledge Graphs with Retrieval-Augmented Generation, showing you how to create systems that are not only powerful, but also explainable, traceable, and enterprise-ready.
You’ll also
• Flowcharts, comparison tables, and prompt templates
• A glossary of terms, debugging tips, and ready-to-use code snippets
• Real-world case studies across healthcare, finance, governance, and more
• A complete KG-RAG API blueprint for production systems
Ready to stay ahead of the AI curve? Build smarter, explainable systems today with the definitive guide to KG-RAG.
Unlock the next evolution of AI development by combining the power of Large Language Models (LLMs) with the structured intelligence of Knowledge Graphs. This comprehensive guide equips you with the theory, tools, and step-by-step workflows to build real-world Retrieval-Augmented Generation (RAG) systems—enhanced with graph-based reasoning, transparency, and scalability.
Whether you're a machine learning engineer, data scientist, AI researcher, or enterprise architect, this book offers a practical roadmap to mastering KG-RAG systems, from foundational concepts to full deployment.
What You’ll Learn
• Core Concepts Made Clear: Understand RAG, Knowledge Graphs, ontologies, embeddings, vector search, and graph-based retrieval.
• Hands-On Projects: Build a working KG-RAG chatbot from scratch using tools like LangChain, Neo4j, Qdrant, and GPT.
• Design Smarter AI Systems: Explore graph-enhanced prompt engineering, semantic retrieval, and modular pipeline architecture.
• Explainability & Trus Learn how to visualize graph paths, track provenance, and ensure transparency in AI outputs.
• Deployment at Scale: Discover best practices for cloud deployment, API design, CI/CD, and system monitoring.
• Security & Ethics: Address risks such as hallucinations, data drift, privacy, and knowledge decay.
Who Should Read This Book?
• AI/ML Engineers building next-gen applications with LLMs
• Data Scientists and NLP Specialists working on retrieval systems
• Knowledge Engineers and Ontology Designers
• CTOs, Product Managers, and Innovation Leads in AI-driven teams
• Researchers exploring hybrid neuro-symbolic architectures
What Makes This Book Unique?
Unlike generic books on LLMs or RAG, this guide goes deep into the integration of Knowledge Graphs with Retrieval-Augmented Generation, showing you how to create systems that are not only powerful, but also explainable, traceable, and enterprise-ready.
You’ll also
• Flowcharts, comparison tables, and prompt templates
• A glossary of terms, debugging tips, and ready-to-use code snippets
• Real-world case studies across healthcare, finance, governance, and more
• A complete KG-RAG API blueprint for production systems
Ready to stay ahead of the AI curve? Build smarter, explainable systems today with the definitive guide to KG-RAG.
228 pages, Hardcover
Published May 16, 2025
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