Check grain before blaming AI

A doubled sales number often comes from joining order and order-line tables at different grains. Define what one row means, whether keys are unique on each side, and how row counts change after the join.

Explain why a relationship is valid

Declare one-to-one, one-to-many, or many-to-many relationships and the required pre-aggregation. Use bridge and deduplication rules for many-to-many data. Distinguish order, shipment, and settlement dates.

Give AI an approved aggregation path

For each metric, define the formula, default grain, allowed dimensions, and prohibited combinations. BuildTable can help organize fields, relationships, and semantics; complex joins and historical models still require POC validation.

Reconcile with small samples

Manually check ten records across detail, aggregate, filter, and time changes. Save test queries, model version, results, and known limits, then rerun them after every model change.

Public references

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