Retrieval-Augmented Generation (RAG) has emerged as a foundational architecture pattern for grounding Generative AI in trusted enterprise knowledge. This paper proposes an Enterprise RAG Architecture that integrates governed content ingestion, metadata, semantic chunking, embeddings, vector search, hybrid retrieval, re-ranking, authorization, prompt orchestration, model routing, grounding, citations, evaluation, observability, and lifecycle governance. The framework connects enterprise information from SAP and non-SAP sources with SAP Business Technology Platform, SAP Business Data Cloud, SAP Datasphere, SAP HANA Cloud, SAP AI services, APIs, and secure integration patterns. It addresses critical enterprise requirements including source-level authorization, privacy, prompt injection defense, stale-content management, retrieval quality, hallucination reduction, model governance, and Agentic RAG. The proposed architecture enables organizations to build scalable and reusable AI assistants and agents that deliver contextual, explainable, secure, and business-aligned responses across hybrid and multi-cloud environments.

Sanjeeve Kumar Gajadi, "Enterprise RAG Architecture" International Journal of Scientific Research and Engineering Development, V9(5): Page(381-384) September - October 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.