RAG at Scale: The Data Engineering Challenges

Retrieval-augmented generation (RAG) has emerged as a powerful technique for building AI systems that can access and reason over external knowledge bases. RAG enabled us to build accurate and up-to-date systems by combining the content-generative capabilities of LLMs with user-context-specific, precise information retrieval.

However, deploying RAG systems at scale in production reveals a different reality that most blog posts and conference talks gloss over. While the core RAG concept is straightforward, the engineering challenges required to make it work reliably, efficiently, and cost-effectively at production scale are substantial and often underestimated.