Technology

It all depends on how and what you ask it, plus an element of randomness. Remember that it's essentially a massive text predictor. The same question asked in different ways can lead it into predicting text based on different conversations it trained on. There's a ton of people talking about python, some know it well, others not as well. And the LLM can end up giving some kind of hybrid of multiple other answers.

It doesn't understand anything, it's just built a massive network of correlations such that if you type "Python", it will "want" to "talk" about scripting or snakes (just tried it, it preferred the scripting language, even when I said "snake", it asked me if I wanted help implementing the snake game in Python 😂).

So it is very possible for it to give accurate responses sometimes and wildly different responses in other times. Like with the African countries that start with "K" question, I've seen reasonable responses and meme ones. It's even said there are none while also acknowledging Kenya in the same response.

Students first need to learn to:

1. Break down the line of code, then
2. Ask the right questions

The student in question probably didn't develop the mental faculties required to think, "Hmm... what the 'f'?"

A similar thingy happened to me having to teach a BTech grad with 2 years of prior exp. At first, I found it hard to believe how someone couldn't ask such questions from themselves, by themselves. I am repeatedly dumbfounded at how someone manages to be so ignorant of something they are typing and recently realising (after interaction with multiple such people) that this is actually the norm^[and that I am the weirdo for trying hard and visualising the C++ abstract machine in my mind].