Retrieval-augmented generation (RAG) enhances the factual accuracy and contextual relevance of large language models (LLMs) by connecting them to external data sources. RAG systems use semantic similarity to identify text relevant to a user query. However, they often fail to explain how the query and retrieved pieces of information are related, which limits their reasoning capability.

Graph RAG addresses this limitation by leveraging knowledge graphs, which represent entities (nodes) and their relationships (edges) in a structured, machine-readable format. This framework enables AI systems to link related facts and draw coherent, explainable conclusions, moving closer to human-like reasoning (Hogan et al., 2021).