Retrieval-augmented generation (RAG) is now the default pattern for grounding large language models in private or domain-specific knowledge. Yet most RAG systems still hallucinate, and the cause is rarely the model itself. It is the retrieval step. A language model can only reason over the passages it is handed; when retrieval returns an incomplete or disconnected set of passages, the model quietly fills the gaps with plausible-sounding but unsupported text. The retrieval layer, in other words, is where trustworthiness is won or lost.This article examines a specific architectural idea — relationship-aware retrieval — and how it addresses the retrieval weaknesses that lead to hallucination. The reference implementation is RudraDB-Opin, a free, relationship-aware vector database. RudraDB-Opin is the free edition built for learning, prototyping, and real projects: it supports up to 100,000 vectors and 500,000 relationships — ample room to model a substantial knowledge base and demonstrate every retrieval pattern discussed here.