According to @godofprompt, graph retrieval-augmented generation (Graph RAG) significantly surpasses traditional vector search by enabling multi-hop reasoning across 3-4 levels of data relationships, making it possible to uncover deep causal links in business questions such as 'Why did revenue drop in Q3?' (source: twitter.com/godofprompt/status/2009545176814084456). While vector search is limited to surface-level associations, Graph RAG can connect complex chains such as revenue → customer churn → product bugs → delayed feature launches, revealing detailed causality. This breakthrough offers AI-driven enterprises a powerful approach to root-cause analysis, enhancing decision-making and driving actionable insights in areas like revenue forecasting, product management, and customer retention.