thanks for this overview. I wanted to highlight two main issues with the setup you outline here:

1. if all the steps (intention, sql creation, error handling, etc.) are handled by a LLM, the cost for one query can outburst the business values it creates. Also the processing time will be not applicable to customer endpoints, since they do now want to wait 10 seconds or more for the answer.

The LLM will always be the slowest part of this process, so one should use it only for parts where no alternative exists or where it outperforms any other methodology by a mile, which from my point of view is just the sql creation itself.

Everything else can be done by other models, that are much faster and will cost less.

2. The biggest elephant in the room are the values in a column. The LLM will never know them, because the schema just includes the column name and the datatype of the column. The user that creates the query will in most scenarios also not be aware of the specific wording that was used in the database. So as a result you should always include a mechanism that checks the sql query beforehand in terms of correct values. you can achieve this with a vector search within the possible list of values for a column for example. Because at the end, the LLM will never get an error message for searchign for a value that does not exist in a table and would just return an empty result.