An LLM cannot answer questions about your data. Its knowledge stops at the end of its training corpus, it never saw your policy documents or anything else sitting in your company's private knowledge base, and even when a fact is in there it cannot reliably find it on demand. Retrieval-augmented generation, or RAG, fixes all three. Find the right passages first, then let the model answer from those. That makes it a data problem before it is a model problem, and Db2 has the pieces: vectors in the same row as the relational content they describe, similarity search in SQL, indexing for scale, language models from the provider you choose, hosted or self-hosted, and connectors for the Python frameworks your application teams already use. I will build a RAG pipeline live with the Haystack connector. A PDF ingested into a Db2 table. Questions answered with page and section citations. Both models self-hosted, no API keys, nothing leaving the environment. Then we open the pipeline component by component, look at what the run actually cost, and cover what I would harden before production. Everything shown is in the Db2 AI cookbook and runs on your own hardware.
