How to Convert SQL Between Database Dialects with AI

Start by naming the source and destination engines, their versions and the relevant schema. Tell the AI whether the task concerns a SELECT query, a table definition or a modifying statement. Those tasks have different compatibility concerns. Include a small example of the expected result, using invented or appropriately sanitized data. Ask for a conversion explanation alongside the proposed SQL. The explanation should identify changed functions, operators, quoting rules, parameter placeholders and assumptions. PostgreSQL, MySQL, SQL Server, BigQuery and SQLite share much SQL vocabulary, but that does not make every expression portable. Separate syntax changes from semantic changes. A function with a similar name may accept arguments in a different order or handle dates differently. Implicit type conversions can also change whether an expression succeeds and which result type it produces. SQLite's documented flexible typing makes it especially important to test against the actual destination schema rather than assuming local success proves portability. Build a comparison fixture containing ordinary rows and relevant edge cases. Include nulls, duplicate keys, empty strings, numeric boundaries and representative timestamps where those values matter. Compare complete outputs: which rows appear, how many times they appear, their values and their types. Define an ordering before comparing ordered results; a row limit without a meaningful order does not specify a stable top set. A translation tool's confidence is not execution evidence. Run the source and destination statements in suitable test databases and investigate every unexpected difference. For writes, check affected rows and stored values in an isolated environment. Keep unresolved differences in a migration checklist rather than declaring equivalence because the new query parses.