Graph RAG combines retrieval augmented generation with knowledge graphs to enhance AI accuracy, offering improved contextual responses in healthcare, finance, and e-commerce by retrieving and applying specific contextual information and adding it to large language models, providing structured, context-rich data for more accurate AI-generated responses. Unlike traditional RAG models, graph RAG incorporates knowledge graphs into the retrieval process, allowing large language models to retrieve and process graph data, making connections between data points and relationships more efficiently than traditional approaches. This technology is valuable across multiple industries due to its ability to map networks of communication in telecommunications, improve diagnostics and treatment recommendations in healthcare, detect fraud in finance, and enhance recommendation systems in e-commerce by analyzing relationships between products, users, and their purchasing behavior.