How Graph Architecture Grounds RAG and Prevents Hallucinations

Hallucinations in retrieval-augmented generation systems are presented as primarily a retrieval and data-structure challenge rather than solely a model or prompting problem, because vector search retrieves semantically similar text fragments without verifying entity identity, logical relationships, or causal connections. The discussion argues that vector-only RAG can fragment context, miss domain relationships, and conflate similar entities, while graph-based retrieval uses typed nodes, explicit edges, and deterministic multi-hop traversal to return connected, traceable subgraphs. FalkorDB is described as combining graph traversal, schema-enforced ontology ingestion, vector search, tenant-isolated graphs, and low-latency querying to support hybrid retrieval in which vectors identify candidates and graph paths verify their relationships. Schema constraints can prevent unsupported relationship types from entering retrieval, but they do not ensure that extracted facts are true, meaning errors are shifted to the more auditable ingestion stage rather than eliminated. Cited benchmarks suggest graph and carefully designed hybrid approaches can improve factuality on complex questions, although results vary by dataset and architecture and naive concatenation of vector and graph context may reduce precision. The approach is positioned as especially valuable in healthcare, legal, financial, compliance, and agentic applications, where inaccurate relationships can have significant operational consequences, while vector-only retrieval may remain adequate for simpler semantic document discovery.

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