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THE AI-NATIVE KNOWLEDGE · GraphRAG: Designing Knowledge Retrieval on Graphs — From Core Concepts to Twelve Real-World Use Cases - Softcover
Synopsis
Your search box can find documents. It cannot answer questions.
Somewhere in your organization there is a question no single passage contains: “Which form does this process need, and who signs it?” — “How many products share this component?” — “What changed since yesterday?” Classic RAG fails on all of them — and it fails quietly, with answers that sound confident and are wrong.
GraphRAG is the engineering discipline that fixes this — and this book teaches it end to end: not as theory, but as a complete, measurable method you can defend in front of your team.
What you will be able to do after reading:
- Decide with evidence, not fashion — classify your real questions, walk a 35-point decision matrix, and know exactly when you need a graph (and when you don’t)
- Build the graph from any source — LLM extraction, rules, database import, and images; entity resolution that never merges the wrong people
- Master the five retrieval patterns — local, global, DRIFT, path traversal, and Text2Cypher — and route every question to the right one
- Ship answers people can trust — citations that open, reasoning paths that are recorded (never invented), and refusals that are honest
- Evaluate and operate for real — golden sets, three-tier diagnosis, incremental updates, cost budgets, and access control that never leaks
- Go agentic when it pays — multi-step research loops with tools, budgets, and guards
What makes this book different:
- Three complete projects built chapter by chapter on realistic synthetic data — an internal knowledge assistant, a support chatbot, and a research assistant — with real token bills, real failures, and real fixes
- Nine industry deep-dives: code assistants, legal contracts, BI, CRM, e-commerce, medicine, fraud detection, news intelligence, and technical manuals
- Runnable companion datasets with 45 expert-keyed test questions and planted traps — so you can reproduce every number in the book
- Self-check quizzes in every chapter, with answer keys
Written for engineers, architects, and technical leaders building AI systems over private data. Python examples throughout; works with NetworkX for learning and Neo4j for production.
Measure first. Decide with criteria. Cite everything. Refuse honestly. That is GraphRAG as this book teaches it — 31 chapters, one method, and a system you can keep honest for years.
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THE AI-NATIVE KNOWLEDGE · GraphRAG: Designing Knowledge Retrieval on Graphs ? From Core Concepts to Twelve Real-World Use Cases
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THE AI-NATIVE KNOWLEDGE GraphRAG
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THE AI-NATIVE KNOWLEDGE GraphRAG : Designing Knowledge Retrieval on Graphs - From Core Concepts to Twelve Real-World Use Cases
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Taschenbuch. Condition: Neu. Neuware - Your search box can find documents. It cannot answer questions. Somewhere in your organization there is a question no single passage contains: 'Which form does this process need, and who signs it ' - 'How many products share this component ' - 'What changed since yesterday ' Classic RAG fails on all of them - and it fails quietly, with answers that sound confident and are wrong. GraphRAG is the engineering discipline that fixes this - and this book teaches it end to end: not as theory, but as a complete, measurable method you can defend in front of your team. What you will be able to do after reading: - Decide with evidence, not fashion - classify your real questions, walk a 35-point decision matrix, and know exactly when you need a graph (and when you don't)- Build the graph from any source - LLM extraction, rules, database import, and images; entity resolution that never merges the wrong people- Master the five retrieval patterns - local, global, DRIFT, path traversal, and Text2Cypher - and route every question to the right one- Ship answers people can trust - citations that open, reasoning paths that are recorded (never invented), and refusals that are honest- Evaluate and operate for real - golden sets, three-tier diagnosis, incremental updates, cost budgets, and access control that never leaks- Go agentic when it pays - multi-step research loops with tools, budgets, and guards What makes this book different: - Three complete projects built chapter by chapter on realistic synthetic data - an internal knowledge assistant, a support chatbot, and a research assistant - with real token bills, real failures, and real fixes- Nine industry deep-dives: code assistants, legal contracts, BI, CRM, e-commerce, medicine, fraud detection, news intelligence, and technical manuals- Runnable companion datasets with 45 expert-keyed test questions and planted traps - so you can reproduce every number in the book- Self-check quizzes in every chapter, with answer keys Written for engineers, architects, and technical leaders building AI systems over private data. Python examples throughout; works with NetworkX for learning and Neo4j for production. Measure first. Decide with criteria. Cite everything. Refuse honestly. That is GraphRAG as this book teaches it - 31 chapters, one method, and a system you can keep honest for years. Seller Inventory # 9798194044313