Ok but you understand that the fundamental nature of LLMs amplifies errors, right? A hallucination is, by definition, a series of tokens which is plausible enough to be indistinguishable from fact to the model. If you ask an LLM to explain its own hallucinations to you, it will gladly do so, and do it in a way that makes them seem utterly natural. If you ask an LLM to explain its motivations for having done something, it will extemporize whichever motivation feels the most plausible in the moment you're asking it.

LLMs can be handy, but they're not trustworthy. "Own and be responsible for the code you commit" is an impossible ideal to uphold if you never actually sit down and internalize the code in your code base. No "summaries," no "explanations."

So your argument is that if people don't use the tool correctly they might get incorrect results? How is that relevant? If you Google search for the wrong query you'll similarly get incorrect results