If you run AI platforms for a bank, an insurer, or anyone else living under a stack of regulations, you already know the use case everyone asks for first: let our compliance and risk teams ask questions against our own policy documents and get an answer they can trust, with a citation they can check.Retrieval-augmented generation (RAG) is the right tool for that job. When a question is asked, instead of answering from memory, the model looks the answer up in your documents and includes a citation.The part slowing teams down isn't the pattern, it's the configuration. A RAG pipeline includes a number of variables: chunk size, overlap, how many chunks to retrieve, which embedding model to use, and how the search itself works. There's no universal best setting. The right combination depends on your documents and your questions, so it can't ship as a default. It has to be discovered, per corpus, and most teams discover it by guessing from a tutorial, eyeballing a few answers, and shipping. In a regulated environment, that's not a comfortable place to be.AutoRAG, an open source tool for finding the optimal RAG pipeline for your data, is a technology preview feature in Red Hat OpenShift AI that replaces guesswork with measurement. You point it at your documents, give it a set of questions with known answers, and it runs an optimization: it sweeps configurations, scores each, and ranks them on a leaderboard. The winning pattern is immediately usable, with an endpoint your applications can call. We built a demo to show the whole path end-to-end, from a folder of policy documents to a compliance assistant answering questions with citations.