This is the problem with using AI for generating SQL statements; it doesn't know the semantics of the your database schema. If you are still open for a solution, I recently deployed a solution[1] that combines AI, db schema and simple way train AI to know your database schema.
Essentially, you "like" correct (or manually corrected) generations and a vectorized version is stored and used in future similar generations. An example could be tell which table or foreign key is preferred for a specific query or that is should wrap columns in quotes.
From my preliminary tests it works well. I was able to consistently make it use correct tables, foreign keys and quotes on table/column name for case-sensitivity using only a couple of trainings. Will open a public API for that soon too.
Essentially, you "like" correct (or manually corrected) generations and a vectorized version is stored and used in future similar generations. An example could be tell which table or foreign key is preferred for a specific query or that is should wrap columns in quotes.
From my preliminary tests it works well. I was able to consistently make it use correct tables, foreign keys and quotes on table/column name for case-sensitivity using only a couple of trainings. Will open a public API for that soon too.
[1]: https://www.sqlai.ai/