RAG-Ready Patterns for Data Platforms

Chapter 5. Curated Grounding: Golden Answers and the Living Glossary

In the previous chapters, we focused on the foundations that make RAG possible in the enterprise: semantically grounded entities, reliable metadata, and pipelines that enforce contracts. Those investments are necessary, but they are not sufficient.

Even with clean, well-modeled data, enterprise questions usually hinge on context that lives outside the platform’s data assets: which definition a team uses, which exclusions are standard, which time windows are canonical, and which source is considered authoritative for a given decision. When that context is implicit, a large language model (LLM) will often produce a response that is plausible and well-written, but is also wrong.

Put differently: the “meaning” behind enterprise data is not inherently present in the data itself. To answer a question reliably, a grounding layer must do three things:

• Interpret the user’s intent.

• Resolve the relevant business definitions.

• Route to the right assets (a semantic model, SQL source, report, wiki, API, runbook, etc.).

This chapter is about making that grounding layer explicit, so retrieval becomes dependable and answers become traceable.

Building on the “smart intern” analogy from Chapter 2, we established that an intern who knows every query language but none of the business context will not be effective. LLMs are similar. They are highly capable, but blind to the tacit assumptions that real decisions depend on. In ...