AI-Refactored SQL Queries Can Silently Drop Rows Even When They Run Faster

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An AI-generated SQL rewrite that swaps a LEFT JOIN for an INNER JOIN may run faster but can silently omit rows where no matching parent record exists, changing query results without any obvious error. This is a common risk in legacy databases where orphaned records — such as orders tied to non-existent customers — are present. A quick visual check of query output is insufficient to catch the data loss, since returned rows still appear valid and complete. Developers are advised to treat AI-generated query changes as unverified patches and validate them by running a differential test that compares the full result sets of the original and rewritten queries. Including edge-case fixtures — such as orphaned rows, parents with no children, duplicates, and NULL values — in the test baseline helps ensure the check catches more than just join-type conversions.

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