The GraphRAG Playbook: Build Intelligent AI Applications with Knowledge Graphs, LLMs, RAG, and Semantic Search (The Knowledge Graph and GraphRAG Playbook Series) - Softcover

Book 2 of 2: The Knowledge Graph and GraphRAG Playbook Series

Barclay, Calvert

9798192629321: The GraphRAG Playbook: Build Intelligent AI Applications with Knowledge Graphs, LLMs, RAG, and Semantic Search (The Knowledge Graph and GraphRAG Playbook Series)

Softcover

ISBN 13: 9798192629321

Publisher: Independently published, 2026

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Build Intelligent AI Applications with Knowledge Graphs, LLMs, RAG, and Semantic Search
Traditional retrieval-augmented generation can provide useful context to large language models, but many complex questions require more than retrieving isolated passages. GraphRAG combines the power of knowledge graphs with retrieval and language models to help AI systems work with connected information, relationships, and broader context.
The GraphRAG Playbook provides a practical guide to understanding and building GraphRAG-powered AI applications. It builds on the concepts behind knowledge graphs and explores how graph-based retrieval can enhance modern RAG and LLM workflows.
You will learn how GraphRAG works, how information can be represented and retrieved through connected structures, and how graph-based context can support more capable AI applications.
Inside, you will explore:

GraphRAG concepts, architectures, and workflows
Knowledge graphs as a foundation for GraphRAG
Graph-based retrieval and contextual search
Combining graph retrieval with traditional RAG
LLM integration and prompt-driven retrieval workflows
Entity, relationship, and multi-hop retrieval
Semantic search and graph-aware information discovery
Building practical GraphRAG pipelines
Designing GraphRAG applications for real-world use cases
Evaluating, improving, and extending GraphRAG systems

Whether you are new to GraphRAG or already familiar with RAG and knowledge graphs, this playbook provides a structured path toward building knowledge graph-powered AI applications with LLMs, retrieval-augmented generation, semantic search, and connected data.

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