Traditional Retrieval-Augmented Generation (RAG) relies on dense vector embeddings to match user queries with semantically similar document chunks. While vector search excels at direct factual lookups—such as retrieving a company’s refund policy or summarizing a specific technical specification—it consistently stumbles …