Retrieval‑Augmented Generation (RAG) combines large language models with up‑to‑date knowledge sources, delivering accurate, contextual evidence at the moment a security questionnaire is answered. This article explores RAG architecture, integration patterns with Procurize, practical implementation steps, and security considerations, equipping teams to cut response time by up to 80 % while maintaining audit‑grade provenance.