Why My High-Stakes RAG Failed and How I Rebuilt it with Deterministic Graphs

The Conflict: Naive RAG relies on semantic similarity (vectors), which is "context-blind" to the rigid logical dependencies and superseding clauses found in high-stakes industries like finance. The Pivot: Moving from "probabilistic vibes" to Knowledge Graphs. By structuring unstructured text into an ontology of nodes and edges, retrieval becomes a deterministic graph traversal rather than a statistical guess. Technical Implementation: The article covers schema-first extraction, utilizing LLMs to generate Cypher queries, and building "Glass Box" audit trails for full transparency. The Result: A shift from 62% to 94% accuracy on multi-hop logical queries, transforming a hallucination-prone chatbot into a verifiable compliance state machine.