Graph Engineering vs RAG Explained

by Vojtech: Microsoft

This is f*cking dangerous. A cheap model on a good graph beats an expensive one on bad retrieval, and Microsoft, Stanford, and MIT each proved it. RAG finds text that looks like your question but cannot find facts that connect. Ask why did this happen and you get fragments, never the chain of causes. Graph engineering flips it: store facts as Subject → Relation → Object, then walk the path from cause to effect, link by link. The graph doesn't just answer; it grows. Every new fact gets written back so the system compounds instead of resetting. Kimi K3 is the engine with 1M context holding the whole evidence chain in one pass at a third of the frontier's price. The model is the easy part while the graph is what makes it work. A full A, Z guide covers eight layers built from scratch.