Enterprise teams want to use large language models in production, but quickly discover that prompt engineering and fine tuning don’t scale: prompts can’t see private knowledge, and fine tuning is slow, expensive, and brittle whenever documents change. That’s why most organizations land on RAG. But today’s implementations are almost all vector based, and they hit hard limits with fine-grained retrieval, multi-hop questions, and global reasoning over large, structured document collections such as financial reports. In this talk, we’ll dissect those limits with concrete examples, and show how GraphRAG on NebulaGraph addresses them by explicitly modeling entities, relations, and communities, and by using graph algorithms for multi-hop and global queries. We’ll then go one step further and introduce Fusion GraphRAG, a document-structure-aware retrieval layer that sits on top of the graph. Fusion Graph indexes metadata, chapters, sections, tables, and figures rather than building a full knowledge graph for every sentence, delivering 5–6× faster indexing while significantly improving answer quality on long, hierarchical documents. Agent developers, platform engineers, and product managers will leave with a practical taxonomy of prompt engineering, fine-tuning, vector RAG, GraphRAG, and Fusion GraphRAG, real trade-offs from production work, and reference architectures for building more reliable, explainable AI and agent systems on top of NebulaGraph.