I would love to see the "depth" of your knowledge in those topics.
You literally proved the OP's point ... thinking you're learning. More like scratching the surface, with lots of invalid data while not being able to recognize what's invalid.
It's like with latest vector of attacks being spamming Github with malware injected in proper looking code in hope of AI to index it.
Then you paste the code because you don't understand it, but you take it as working and only doing what you've asked for.
This feels like an impossible assessment - yes, a model probably can't give you the education that a advanced/expert book on a topic will, but implying that having a verifiable goal is somehow fake learning feels like an intractable problem.
What level of evidence would be sufficient for you to accept that a model may be able to teach a concept?
I'm happy to take on this challenge with a topic of your choosing, but I don't believe there will be an evidence base that satisfies you that the knowledge is earned or deep enough.
I really wonder where this assessment is coming from? It's not that I use LLM written code for something (in those exercises at least), in fact I don't let the LLM write code (see my prompt example).
It's about guiding me in _doing_ exercises so I learn and I can evaluate if I learned something if I can apply the learning myself.
You literally proved the OP's point ... thinking you're learning. More like scratching the surface, with lots of invalid data while not being able to recognize what's invalid.
It's like with latest vector of attacks being spamming Github with malware injected in proper looking code in hope of AI to index it.
Then you paste the code because you don't understand it, but you take it as working and only doing what you've asked for.