One Follow-Up Prompt Can Catch the Data Bugs Your Code Review Misses

A piece published on DEV Community argues that data engineers routinely skip a critical step when using AI coding assistants: asking the model to critique its own output. The author illustrates the risk with an incremental load scenario, where an engineer catches two obvious flaws through self-review but misses a third — an unstable business key assumption — that later causes unexplained duplicate rows in production. The core argument is that prompting an AI to find problems generates fundamentally different output than prompting it to solve one, because the tasks are shaped differently. The recommended follow-up prompt — 'Critique this. What breaks first? What did you assume that I didn't tell you?' — is designed to surface hidden assumptions the engineer never explicitly provided. The author notes this habit is most absent among intermediate engineers, who have learned to question AI output but still treat the entire review process as solely their own responsibility.