[kbuchanan]

I agree this is a problem too, but I suspect mostly for novel(ish) software problems. For me, LLMs have expanded the solution space, because, while I used to be decent with SQL in Postgres, now I'm operating on a whole new level—the LLM's ability to make sense of Postgres' full suite of options, and the performance implications of the queries, is far beyond what I could have accomplished.

[aselimov3]

That’s a good point but I think this article above would push back on this point slightly. I certainly am able to do a lot more with LLMs because it can produce a passable solution in a lot of places but I’m not sure it expands my solution space really. I tend to separate what I’m able to build and what I’m able to solve. I can build a frontend with an LLM but I don’t think I’m able to solve frontend engineering problems.

[CharlieDigital]

It's a mixed bag.

I've been working with Apache AGE (openCypher in Postgres) recently and found that left to its own device, the agent wrote terribly inefficient queries, even when given a test harness and instructions to examine the result of the query plan.

It just didn't seem to understand the graph traversal, even when given the graph schema and small snippets.

I ended up hand-writing the structural "skeleton" of the main query that I performance tuned to a certain extent and then handed it over to Codex to finish off. Once it had this skeleton to start from, it was able to do a much, much better job of writing this query.