There is an interesting point between the lines here. And I’m pretty extremely on team “a probabilistic word generator is not intelligence”

Natural language to sql has been a bit of a flop so far, with plenty of paid and open source solutions. The reason IMO is because they require clean well defined data and most companies who want natural language to report output want to run it on the usual hot mess of a DB set up

In the LLM world this won’t work

In a theoretical AGI world all you would need is feed the AGI the insert logic, feed it the full git history, and it would understand everything, it would rename mislabeled columns, it would clean bad data up, and it would be able to create any metric from natural Language

But we have word generators, not AGI, so we end up with mostly useless libraries for this topic specifically