The hard part of Text2SQL is detecting believable wrong answers
Text2SQL turns everyday questions into database queries, but s struggle to detect answers that look reasonable and are still wrong. There is no clear standard for how large a reference set of known questions and answers should be or how often teams should update it. Comparing generated queries with expected SQL word for word is too strict because different queries can produce the same correct answer.
Comparing the returned results is more realistic, but repeatedly running queries can become costly at scale. provides another check but adds operational work. The hardest failure is a query that runs successfully and returns a believable number that does not match the intended business meaning.
Teams also need rules for unclear questions or low- answers, such as asking a follow-up question, requesting review, or refusing to answer. Tools such as still require a decision about whether correctness should be judged from the query, its returned data, or the user's real business intent.
Key points
- Word-for-word SQL matching can reject correct queries written in a different form.
- Comparing returned data is more realistic, but it may be expensive at large scale.
- A successful query can still return a believable number that misses the business intent.
- Low- or unclear questions need a defined path for follow-up or .
- Total cost includes reference data , query execution, and review as well as token use.