Graph Engineering Replaces RAG: Insights from Microsoft, Stanford, Anthropic
Traditional RAG struggles with complex multi-entity questions. Microsoft, Stanford, and Anthropic independently discovered that graph engineering (knowledge graphs) can improve accuracy by 18% and reduce costs by 85%. Microsoft's GraphRAG converts unstructured text into a knowledge graph, achieving 18% higher accuracy and 85% lower token costs. Stanford's DSPy and STORM treat the model as a node in a graph, and scaling law research shows a small model with a good graph outperforms a large model with a poor graph. Anthropic's Claude, combined with MCP, enabled LaunchNotes to detect incidents 5x faster and cut meeting time by 50%. The article details the full pipeline, five prompt templates, and five business applications. Ideal for engineers advancing beyond basic RAG.