RAG-Ready Patterns for Data Platforms

Chapter 7. Building RAG-Ready Data Products

In every organization, there are two ways to find an answer from data. You can go to the shelf: pull up a dashboard, scan a catalog, run a query you found in a wiki. Or you can go to the expert, someone who knows which table to use, what its quirks are, which filters matter for your specific question, and what the numbers actually mean.

The shelf is organized, labeled, and discoverable. The expert is something else entirely. The expert’s knowledge does not live in one place. It is assembled on the fly from multiple sources: the catalog entry they memorized years ago, the pipeline behavior they learned from an incident last month, the access constraint they negotiated with the privacy team, the grounding question a stakeholder asked just yesterday. The expert is a federation of knowledge, not a monolith.

When we built data products for the dashboard era, we built them for the shelf. Well-organized, clearly labeled, findable if you know what you are looking for. But an AI system that must act as the expert cannot browse a shelf. It needs to federate the same knowledge the expert carries, drawn from the asset catalog, the serving layer, the governance system, the quality engine, and the grounding function, into a single coherent view of each data product.

This is the problem this chapter solves: how to package and compose a RAG-ready data products so that they are not merely queryable, but understandable by AI systems that must reason ...