Large language models are only as useful as the context you give them. For data engineers, that context is your schema, your lineage graph, your query history, your dbt models — and most teams haven't connected those dots yet.
This session is a practical introduction to Retrieval-Augmented Generation from a data engineering lens. We'll cover what RAG actually does under the hood, why it matters more for data teams than most, and the four places it shows up naturally in the data engineering workflow: answering schema questions without digging through a stale catalog, grounding SQL agents in your actual table definitions, giving incident response agents access to historical pipeline context, and surfacing institutional knowledge that currently lives only in senior engineers' heads.