The Anatomy of a Trustworthy AI Answer: A Comparative Experiment for RAG Architectures

Abstract

Retrieval-Augmented Generation (RAG) has become the go-to fix for LLM hallucinations. But its most common form, built on Vector Databases, is like a confident consultant who has only read the executive summaries. It's fluent, convincing, and adept at finding information that sounds right, but critically lacks the deep, verifiable connections between the facts. In high-stakes domains like medicine, this creates a dangerous new form of AI: one that is wrong with conviction. This paper provides a comparative experiment for distinguishing between answers that merely sound correct and those that are verifiably true. Our head-to-head evaluation of Vector-based versus Knowledge Graph-based RAG reveals a stark architectural choice. Our findings demonstrate that while Vector RAG produces a convincing but untraceable story, the Knowledge Graph approach delivers a factually correct answer with a verifiable evidence trail. This is the blueprint for building RAG systems that don't ask for your trust - they earn it by showing their work.