Production RAG pipeline

Retrieval-augmented generation (RAG) is a technique where a language model answers using documents retrieved from your own data at query time, instead of relying only on what it learned in training. A production RAG pipeline is the engineered system around that — ingestion, chunking, embeddings, a vector store, retrieval, and guardrails — built to run reliably rather than as a demo.

RAG is how an assistant answers questions about your specific content — a policy library, a product catalogue, a documentation set — with citations, and without retraining a model. The difference between a weekend demo and production is everything around the model: how documents are chunked, how retrieval is tuned to the real corpus, how answers are grounded and evaluated, and how the whole thing is observed when it drifts.

We build these on Cloudflare (Vectorize, Workers AI, D1) with evaluation harnesses wired into the deploy, so a regression fails loudly instead of quietly degrading.