Why RAG Is Failing at Complex Questions (And How Knowledge Graphs Fix It)

Retrieval-Augmented Generation solved the hallucination problem. Then everyone discovered it can't actually answer hard questions. The issue isn't the LLM. It's not even the retrieval mechanism. It's that traditional RAG treats your knowledge base like a bag of disconnected sentences, when the information you need is buried in relationships spanning multiple documents. GraphRAG is the architecture that's quietly becoming the answer to RAG's biggest limitation. The Multi-Hop Problem Here's a question that breaks standard RAG: "What scientific work influenced the mentor of the person who discovered the double helix structure of DNA?" A traditional RAG system would: Search for "double helix structure DNA discovery" Find chunks mentioning Watson and Crick Maybe find something about their mentors Fail to connect the dots about who influenced those mentors Generate a vague or incorrect answer The problem? This requires connecting information across three hops. First, Watson and Crick discove