The GraphRAG Playbook

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