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High quality retrieval does not ensure correct reasoning. Most RAG pipelines fail because they optimize for finding the document but ignore extracting the answer.

Retrieval is like a librarian handing you the right book. Grounded reasoning is like a researcher actually reading the page and synthesizing the answer without guessing.

When you build over a data lake, you deal with heterogeneous data. This means your AI is jumping between PDFs, CSVs, and logs that lack a strict organizational schema.

If the system finds the correct document but still gives the wrong answer, you have a reasoning gap.

I condensed the LakeQuest framework and its 9,846 QA pairs into a 12-page visual field guide below.

The guide includes a diagram of the evaluation pipeline to help you identify exactly where your system is breaking.

How do you currently distinguish between a retrieval failure and a reasoning failure in your QA process?

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