I wonder if, for a given dialect (and even DDL), you could use that token masking technique similar to how that Structured Outputs [1] thing went:
Quote:
"While sampling, after every token, our inference engine will determine which tokens are valid to be produced next based on the previously generated tokens and the rules within the grammar that indicate which tokens are valid next. We then use this list of tokens to mask the next sampling step, which effectively lowers the probability of invalid tokens to 0. Because we have preprocessed the schema, we can use a cached data structure to do this efficiently, with minimal latency overhead."
I.e. mask any tokens that would produce something that isn't valid SQL in the given dialect, or further, a valid query for the given schema. I assume some structured outputs capability is latent to most assistants nowadays, so they probably already have explored this
Quote: "While sampling, after every token, our inference engine will determine which tokens are valid to be produced next based on the previously generated tokens and the rules within the grammar that indicate which tokens are valid next. We then use this list of tokens to mask the next sampling step, which effectively lowers the probability of invalid tokens to 0. Because we have preprocessed the schema, we can use a cached data structure to do this efficiently, with minimal latency overhead."
I.e. mask any tokens that would produce something that isn't valid SQL in the given dialect, or further, a valid query for the given schema. I assume some structured outputs capability is latent to most assistants nowadays, so they probably already have explored this
[1] https://openai.com/index/introducing-structured-outputs-in-t...