Right, but obviously you can see the unknown unknown problem or Dunning-Krueger popping up when software developers who don’t know everything (unlike me, of course) start using it extensively without knowing the ways in which their prompt engineering is flawed.

One of the big open questions in LLM-assisted learning is whether it helps or hinders less experienced developers.

At this point I'm pretty confident it helps them. They need to understand that it's not infallible, but I think most people figure that out pretty quickly after using it for more than a few days.

There's absolutely a skill to using these things well, and it's a difficult one to teach because it's based more on intuition that you develop over time.

The key thing is helping people understand the strengths and limitations of these tools. That's something I do think we can teach.

>The trick to using it effectively is to figure out the ways of prompting it that produce GOOD results

Might as well just learn to code then.

My argument is that AI-assistance makes it easier and faster to learn to code.

it's not just good prompts vs bad prompts, there's luck involved, since the result is determined by dice rolls.

I used to be smug about getting good results from GPT while coworkers failed to get it to produce anything useful, but over time my luck changed, and GPT stopped nailing it. If you remember there was a bunch of conspiracy talk that GPT got lobotomized, was no longer writing full programs anymore etc, but what really happened is people who got used to rolling 7s started rolling 2s and 3s and shook their fists in anger at their change of luck.