You left out the "for common algorithms like this" part of my comment. None of what you said applies to learning simple, well-established algorithms for software development. If it's history, biology, economics etc. then sure, be wary of LLM inaccuracies, but an algorithm is not something you can get wrong.
I don't personally know much about DHTs so I'll just use sorting as an example:
If an LLM exlains how a sorting algorithm works, and it explains why it fulfills certain properties about time complexity, stability, parallelizability etc. and backs those claims up with example code and mathematical derivations, then you can verify that you understand it by working through the logic yourself and implementing the code. If the LLM made a mistake in its explanation, then you won't be able to understand it because it's can't possibly make sense; the logic won't work out.
Also please don't perpetuate the statistical parrot interpretation of LLMs, that's not how they really work.
I don't personally know much about DHTs so I'll just use sorting as an example:
If an LLM exlains how a sorting algorithm works, and it explains why it fulfills certain properties about time complexity, stability, parallelizability etc. and backs those claims up with example code and mathematical derivations, then you can verify that you understand it by working through the logic yourself and implementing the code. If the LLM made a mistake in its explanation, then you won't be able to understand it because it's can't possibly make sense; the logic won't work out.
Also please don't perpetuate the statistical parrot interpretation of LLMs, that's not how they really work.