When Does Graph RAG Actually Add Value? A Hands-On Experiment

An experiment benchmarked four AI retrieval architectures: plain Retrieval-Augmented Generation (RAG), a graph-only system, a combined Graph RAG, and a full-context frontier model. The goal was to understand the trade-offs and determine when the added complexity of a knowledge graph is justified for information retrieval. While the full-context model performed best overall, the results demonstrated that Graph RAG consistently outperformed traditional RAG on specific types of questions that required understanding relationships across documents. The experiment concludes that the value of a graph-based approach is highly dependent on the specific problem, rather than being a universally superior method.