GraphRAG in the Enterprise

Retrieval-Augmented Generation (RAG) is a commonly utilized pattern for grounding large language models in enterprise data. Instead of solely relying on a model’s training, RAG collects relevant information from internal sources, documents, knowledge bases, and other systems; it then uses that context to guide generation. This approach improves accuracy, allows models to work with proprietary or frequently changing data, and has made RAG a natural starting point for many enterprise AI initiatives. However, as RAG is applied to more complex use cases, its limitations start to surface. Since it treats knowledge as flat chunks of text, it makes it difficult to convey relationships, maintain consistent context, or support multi-step and cross-document reasoning.