A Practical Guide to Testing and Fixing AI Data Products

Effective AI data products require validation beyond successful SQL execution because technically valid outputs can still conflict with an organization’s business logic or omit important information. The guidance emphasizes using subject-matter experts to test products with real workplace questions, as these experts can assess both accuracy and completeness and identify unsupported use cases. When incorrect answers arise, teams should investigate product metadata, column descriptions, and view logic in that order, since unclear business definitions and field documentation are often more common causes than SQL defects. Metadata should be version controlled so past agent responses can be traced to the definitions in effect at the time, while governance and role-based access controls should be established before deployment to prevent overly broad exposure of sensitive data. The recommended process is iterative: build and document the product, test it with experts, correct failures, retest, define access controls, and retain versioned records of its logic.

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