Building Responsible RAG Architecture for Enterprise Data

### Introduction Retrieval-Augmented Generation (RAG) has emerged as the standard pattern for grounding Large Language Models (LLMs) on enterprise domain data. However, moving from a simple prototype to an enterprise-grade production RAG system requires solving critical engineering challenges: retrieval accuracy, document attribution, chunking strategies, and security. ### Core Architectural Components 1. **Hybrid Vector & Keyword Search**: Combining dense vector embeddings (e.g., OpenAI text-embedding-3) with sparse BM25 keyword index matching improves retrieval precision for technical jargon, SKUs, and proper nouns. 2. **Chunking Strategies**: Static token chunking often destroys context. Utilizing semantic chunking based on document structure (headers, paragraphs) produces higher quality embedding vectors. 3. **Citation & Attribution Engine**: Every generated answer must map back to exact source document coordinates (file, page, paragraph) to allow auditing and prevent hallucinations. 4. **Data Isolation & Access Controls**: Ensuring that users only retrieve search results from documents they have explicit authorization to view within the application's RBAC matrix. ### Conclusion RAG systems are not simple database plug-ins; they are multi-stage data pipelines that require rigorous evaluation, provenance tracking, and security architecture.