Reducing RAG Hallucinations With Relationship-Aware Retrieval

Most AI systems that use retrieval-augmented generation (RAG) still produce hallucinated answers — not because the language model is flawed, but because the retrieval step delivers incomplete context. Traditional vector databases only find passages that look similar to a query, missing passages that are causally or structurally connected. RudraDB-Opin solves this by modeling explicit typed relationships between content chunks, allowing retrieval to follow causal, hierarchical, and sequential links that similarity search completely misses.