Glossary
Retrieval-Augmented Generation (RAG) for Analytics
Retrieval-augmented generation is the pattern of giving an LLM relevant context at query time so it does not have to guess. For document Q&A, retrieving chunks of text works well. For analytics, retrieving governed metric definitions, business rules, and entity relationships works dramatically better than retrieving unstructured SQL or docs.
Why naive RAG over schemas does not work
Embedding your schema and retrieving the closest table to a question is a parlor trick. Real analytics questions depend on business logic that is not in the schema, and the model still hallucinates joins between the tables retrieval handed it.
Structured retrieval over a context layer
Retrieve governed metric definitions, dimension descriptions, and business rules that match the question intent. Then reason over them. This is the pattern AlchemData uses under the hood, and it is the reason answers stay grounded even when the question is phrased in an unexpected way.
Related Resources
Glossary
What is a Context Layer? (The Missing Piece in AI Analytics)
A context layer captures the business logic, exceptions, and analyst knowledge an LLM needs to produce trusted answers. It is the piece most generic AI analytics tools skip.
Glossary
What is a Semantic Layer? (And Why It Matters for AI Analytics)
A semantic layer is the governed translation between raw warehouse tables and the business questions people actually ask. In the AI era, it is the layer that decides whether an LLM hallucinates or answers correctly.