What are the real world applications for NL-to-SQL?

Curious to learn more about your use case. If fine-tuning is only ineffective for your most complex queries (and presumably those are less frequent as well, since you mentioned you have few examples), then couldn't you use fine-tuning to handle the simpler queries (presumably the lion's share) and thus free up excess man hours to focus on the more complex queries? Is there any benefit to AI being able to answer 90% of queries vs 0%?

These tools are already fantastic at our 80% average case, even without fine tuning. We are seeing some value, but the real pain is in that other 20%.

Nice! how's the switch to Ada been? Any unexpected hurdles?

Great since GPT4. However, I need to add some optimization because it's take time for the AI to explore databases / tables schema / structures.

Do you still use this? Thoughts?

Is there a reason why an LLM can't learn these nuances the same as a human?

Love this! I don't think you could be more right about the practical challenges of implementing something like this. In my experience, this same problem is what makes it so challenging to onboard new data scientists/analysts.

It takes a lot of training to get a team member up to speed - with the same amount of training, do you think an LLM can compete?