Use Graph RAG to Produce Comprehensive, Context-Aware Answers from Generative AI Models

Graph retrieval-augmented generation (Graph RAG or GRAG) is a collection of techniques that combines the strengths of knowledge graphs with generative AI (genAI) models to create new applications and enhance the capabilities and precision of large language models (LLMs). Put simply, graph RAG exploits the contextual information from knowledge graphs to reduce hallucinations from genAI applications, obtain new insights, increase efficiency, make more accurate predictions, and optimize their decision-making processes.