Your RAG agent seems smart until you ask it to count. "How many items match X?" It answers "about 45 to 50" when the real number is 133. Vector search finds text that looks similar; it cannot count, add things up, or follow relationships across your data. The cause is architectural. RAG pulls chunks of text that resemble your question and asks the model to write an answer from them. That is fine for looking one thing up, but it breaks on counting, totals, multi-step questions, and "is this even in my data" checks. The fix is to build a knowledge graph from your documents automatically, then turn each question into an exact query against that graph, so the model reports real numbers instead of inventing them. You see two agents take the same questions on the same data side by side: one makes up results, the other gets them right every time. You leave knowing how to build it, when to use it over plain RAG, and how to combine both. All code is open source.

Outline: • The RAG Hallucination Problem • Graph-RAG Architecture • Live Implementation • Production Patterns • Decision Framework