Mike & Tommy tackle the uncomfortable truth about AI-generated data pipelines — how code that looks clean and runs without errors can still deliver confidently wrong results. Drawing from this article, they explore why silent failures in joins, filters, and business rules are harder to catch than broken code, and what Power BI and Fabric teams must do to validate outputs before bad data reaches their reports.

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