AutoRAG pipeline optimization in Red Hat OpenShift AI

If you're an AI or machine learning engineer, you've seen the classic demo: ingest a clean PDF into a prototype, build a vector database index using standard tutorial code, and submit a simple question. The model returns a pristine answer, everyone nods, and the project gets greenlit.Then it meets enterprise reality.Six months into production, as your knowledge base expands to thousands of multicolumn technical manuals, tabular financial reports, scanned legal contracts, and shifting internal policies, retrieval accuracy collapses. Users start…

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