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Thanks for replying, that's a perspective I didn't consider. The capability to "talk to your data" just seems so enticing as a solution that I was tunnel-visioned into that UX. If I'm understanding correctly, what you're suggesting is more of a SQL assistant to help people write the correct SQL queries instead of writing the entire SQL query from scratch to answer a generic natural-language question?

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I believe that is what they are saying.

I have found LLMs to be extremely helpful in mapping between schemas as well as helping me formulate queries where, because of decay, data in tables and column names, etc don't map to what you think they would.

You need to provide as much context as you can to the LLM. So full schema definitions, and histographic summarization and samples from the tables themselves.

I hear what you're saying, and actually agree that - unfortunately - is the current state of LLMs today. But I think OP still has a point, and I'd argue as well that that is the aim of GAI, to be able to take a simple question, understand and produce the desired results.

As it stands, and as you mentioned, the current generation of LLMS seem a long way from there. Thus the need for prompt engineers. It'll be nice to see how long they take to crack this problem.

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