I just built a system that beats Basic RAG on every single metric simultaneously. Higher accuracy. Better semantic similarity. 94.6% fewer tokens. Here's exactly how I did it. The Problem Every time an LLM answers a question, it burns tokens. And tokens cost money. The industry's current answer is RAG (Retrieval Augmented Generation). Instead of sending the LLM your entire knowledge base, you retrieve the most relevant chunks and send only those. But here's what nobody tells you: Basic RAG doesn't actually solve the problem. It just moves it. When I built my submission for the TigerGraph GraphRAG Inference Hackathon, I set out to prove something: a knowledge graph can do what vector search cannot. It retrieves precisely what an LLM needs, not just what looks similar. The result was a system that achieves 94.6% fewer tokens than Basic RAG while being more accurate. Here's the full story.…