The GraphRAG Manifesto: Adding Knowledge to GenAI

We're on the cusp of realizing that autoregressive LLMs alone won't suffice for making useful decisions with GenAI, and instead, we need to bring knowledge about things into the mix. This is where GraphRAG comes in - a technique that uses both vector-based RAG and fine-tuning, but also incorporates knowledge graphs to provide more accurate and complete answers. GraphRAG offers several advantages, including higher accuracy, easier development, and better explainability. It's particularly useful for applications where answer quality is essential, such as customer service or legal documents. With the Neo4j LLM Knowledge Graph Builder, users can create a knowledge graph in just a few clicks from unstructured text sources like PDFs, web pages, and YouTube videos. As GenAI progresses, knowledge graphs are becoming increasingly important for applications where answer quality is essential, explainability is needed, or fine-grained controls over access to data are required.