Building a RAG Pipeline on Your Existing Data Warehouse
· 6 min read
The most common failure mode in enterprise AI projects is asking an LLM questions about your business data and getting confidently wrong answers. The model doesn't know your revenue figures, your customer data, or your internal processes — it only knows what it was trained on.
Retrieval-Augmented Generation (RAG) fixes this by giving the model the relevant context it needs at query time, retrieved from your actual data. The surprising part: you probably don't need a new data infrastructure to do it. Your existing warehouse already has the data — you just need the retrieval layer on top.