Graph RAG for AI Applications: Building Knowledge-Aware Intelligent Systems with Graphs, Embeddings, and LLMs

By: Zion, Damian

This book turns hard-won patterns into repeatable frameworks. Every chapter includes runnable Python, Cypher, and API snippets, with guardrails (hop coverage, two-citation evidence, as-of dates) that make systems reliable-not just impressive demos.

About the Technology
Traditional RAG stalls on ambiguity, multi-hop reasoning, and governance. Graph RAG fuses knowledge graphs (entities, relations, time, authority) with vector retrieval so LLMs fetch the right context, explain their answers, and obey policy. You'll learn to scope queries with graphs, retrieve inside that scope with hybrid ranking, and construct compact, faithful prompts.

What's InsideArchitecture blueprints: context engine, session memory, caching, and eval gatesExtraction & graph build: NER/RE pipelines, ontology/shape design, ingestion CIHybrid retrieval: dense + sparse + graph priors, query planning, context rankingFaithfulness & safety: validators, evidence packs, constrained edit/abstain loopsMultimodality: diagrams and tables as first-class evidence (captions & rowsets)Agents & planning: task graphs, preconditions/effects, policy-constrained executionScaling & ops: latency budgets, snapshots/rollbacks, observability with OTelGraph-native tuning: path-conditioned prompts, lightweight LoRA adapters
Who this book is forDevelopers & Data Scientists building production RAG featuresML/Platform Engineers responsible for latency, cost, and reliabilityArchitects & Tech Leads defining knowledge-centric AI roadmapsResearchers/Students seeking practical, evaluable techniques beyond demos
LLMs alone are no longer a moat. Teams adopting knowledge-centric infrastructure are cutting tokens, raising faithfulness, and shipping features faster. If your org can't explain why an answer is true-or roll back a bad knowledge push-you're already behind.

One bad answer can cost more than this book 100 over. These patterns reduce hallucinations, stabilize latency, and make audits trivial. Expect fewer tokens per answer, fewer incidents, and faster, safer deploys-because knowledge is versioned, measured, and portable.

Build AI your stakeholders can trust.
Grab Graph RAG for AI Applications now, wire up the Context Engine in your stack this week, and ship knowledge-aware features that are accurate, explainable, and production-ready.

• Format: Paperback
• Language: English
• ISBN: B0G1BM8RMQ
• ISBN13: 9798273467576
• Release Date: November 2025
• Publisher: Independently Published
• Length: 526 Pages
• Weight: 1.83 lbs.
• Dimensions: 1.1" x 6.7" x 9.6"