Enterprise RAG Architecture: Building a Secure, Scalable Foundation
To move from pilot to production, your Enterprise RAG architecture must be secure, scalable and well managed. This article dives into the architectural elements and design-principles you should follow to build a strong foundation for Enterprise RAG. Core architectural layers Data ingestion & indexing Collect internal data: documents, databases, emails, logs. Pre-process: tokenise, embed, generate vector representations. Index in vector database or semantic search platform. Retrieval engine Accepts query, converts to embedding, retrieves top-k relevant docs. Re-ranking, filtering, metadata controls. Augmentation layer Selected retrieved context is joined with query and possibly prompt template. Policy controls: only authorised data, redaction filters. Generative layer (LLM) Receives augmented prompt and returns output. May include cite/source embedding, traceability. Governance, monitoring & security layer Access c...