Retrieval-Augmented Generation (RAG) is quickly becoming the standard enterprise pattern for deploying large language models (LLMs). Instead of relying solely on pretraining, RAG enriches prompts with fresh, domain-specific information. The result? More accurate answers, fewer hallucinations, and outputs that enterprises can trust.
But building RAG at enterprise scale is tricky. You’re not embedding a few PDFs anymore—you’re embedding billions of rows from databases, log streams, or knowledge repositories. That leads to a critical architectural question: