Graph RAG in the Wild: Insights and Best Practices from Real-World Applications

Retrieval-Augmented Generation (RAG) enhances Large Language Models (LLMs) by enabling access to external knowledge without retraining. While effective, traditional RAG methods—typically reliant on vector-based retrieval face limitations in understanding complex semantics, connecting dispersed information, and supporting user-centric search workflows. Graph Retrieval-Augmented Generation (Graph RAG) addresses these challenges by incorporating knowledge graphs into the retrieval process, enabling semantically enriched and structured query handling. This paper explores the application of Graph RAG across seven real-world domains, including legal compliance, customer support, enterprise knowledge management, finance, education, data protection enforcement, and time series analytics. For each use case, we outline the distinct challenges, solutions, and design decisions made. In addition, we introduce a modular Graph RAG Engine to support ingestion, graph construction, hybrid retrieval, and LLM orchestration. We present empirical evidence demonstrating improvements in accuracy, latency, and user trust, and offer a practical design playbook for making schema choices, selecting retrieval strategies, and constructing prompts. Additionally, we address cross-domain challenges such as graph drift and evaluation strategies. These contributions aim to guide researchers and practitioners beyond traditional RAG and to inspire further research at the intersection of generative AI and knowledge graphs.