Essential GraphRAG

Summary

With RAG, you tap into a trusted data source at runtime to generate accurate LLM responses grounded in real-world information. 

But RAG implementations tend to focus only on unstructured data rather than a structured source. Due to this, LLM responses might lack depth and nuance. You run a greater risk of hallucinations and providing incomplete information. 

However, you can model your data with a knowledge graph, a technique called GraphRAG. This bridges structured and unstructured data to feed your LLM interconnected data. Your LLM gets the context needed to improve RAG performance, accuracy, and traceability.

Check out Essential GraphRAG, a comprehensive guide from Manning on how to build a GraphRAG system from scratch. 

You’ll learn how to:

• Work with basic RAG concepts like vector similarity
• Use advanced techniques like query rewriting and parent doc retrieval
• Construct a knowledge graph with an LLM
• Build Agentic AI with RAG
• Evaluate RAG performance and accuracy