I wish you luck in refining your differentiation.

Side note: I don't see a license anywhere, so technically it isn't open source.

But honestly, most queries I’ve ever seen are just simple joins, which shouldn’t take you 5 minutes to write.

> you don’t need to understand the normalizations

You definitely should. Normalizing isn’t that difficult of a concept, Wikipedia has terrific descriptions of each level.

As to the rest, maybe read docs? This is my primary frustration with LLMs in general: people seem to believe that they’re just as good of developers as someone who has read the source documentation, because a robot told them the answer. If you don’t understand what you’re doing, you cannot possibly understand the implications and trade-offs.

And I could potentially see LLMs being useful to generate the “bones” of a query for me but I’d never expose it to end-users (which was what I was playing with). So instead of letting my users do something like “What were my sales for last month?” I could use LLMs to help build queries that were hardcoded for various reports.

The problem is that I know SQL, I’m pretty good at, and I have a perfect understanding of my company’s schema. I might ask an LLM a generic SQL question but trying to feed it my schema just leads to (or rather “led to” in my trials before) prompt hell. I spent hours tweaking the prompts, feeding it more context, begging with it to ignore the “cash” column that has been depreciated for 4+ years, etc. After all of that it still would make simple mistakes that I hard specially warned against.

I do this regularly and find it very enlightening. After I’ve read a news article or done my own research on a topic I’ll ask ChatGPT to do the same.

You have to be careful when reading its response to not grade on a curve, read it as if you didn’t do the research and you don’t know the background. I find myself saying “I can see why it might be confused into thinking X but it doesn’t change the fact that it was wrong/misleading”.

I do like when LLM‘s cite their sources, mostly because I find out they’re wrong. Many times I’ve read a summary, then followed it to the source, read the entire source, and realized it says nothing of the sort. But almost always, I can see where it glued together pieces of the source, incorrectly.

A great micro example of this are the Apple Siri summaries for notifications. Every time they mess up hilariously I can see exactly how they got there. But it’s also a mistake that no human would ever make.