Spotify ran into a scaling wall when teams kept pinging data experts for insights. Dashboards weren’t enough, and there were over 70,000 datasets in petabytes of data. Simply dumping all schemas into an LLM failed—context windows cap out and raw schemas don’t explain business nuances. To bridge that gap, Spotify built Vedder, a data assistant that picks the right context, writes SQL, runs it, and returns both answers and the queries that generated them. It plugs into Slack, IDEs and a web UI, and more than 2,100 employees have held 13,000+ conversations with it since August 2025. The secret sauce isn’t a bigger model but a curated “cluster” layer owned by domain teams. Each cluster groups relevant warehouse tables with profiling and samples; vetted question-SQL examples written or approved by experts; and business docs that spell out terminology and gotchas. Those curated examples are the few-shot seeds that teach the model how to query and interpret data. An experiment that auto-generated examples from query logs produced a deluge of noisy, misleading pairs—only 12.5% passed expert review. Human judgment remains the gatekeeper of trust. To keep clusters accurate as data evolves, Vedder tracks health scores using signals like schema drift, broken examples and real-world usage. Domain experts see dashboards flagging which clusters need attention. Every interaction with Vedder—questions, generated SQL, user feedback—feeds back into cluster dashboards. That loop lets experts refine context continuously. Spotify’s architecture isn’t tied to its tech stack. Any organization with domain experts and a data warehouse can layer in curated context, so LLMs deliver reliable insights instead of guesses.