I think that's actually deeply different. If a human keeps on apologizing because they are being caught in a lie, or just a mistake, you distrust them a LOT more. It's not normal to shrug off a problem then REPEAT it.
I imagine the cost of a mistake is exponential, not linear. So when somebody says "oops, you got me there!" I don't mistrust them just marginally more, I distrust them a LOT more and it will take a ton of effort, if even feasible, to get back to the initial level of trust.
I do not think it's at all equivalent to what "Real humans" do. Yes, we do mistake, but the humans you trust and want to partner with are precisely the one who are accountable when they make mistakes.
You seem to have a different understanding of what it means in the context of neural networks.
Real humans will not make up non existent api and implement a solution with it, (unless they do it on purpose).
Unfortunately, individual people are not anywhere as reliable as a compiler for ensuring compliance to reality. We are particularly susceptible to flattery and other emotional manipulation, which LLMs frequently employ. This becomes particularly problematic when you ask for feedback on an idea.
In that case, a useful hack is to frame prompts as if you're an impartial observer and want help evaluating something, not as if the idea under evaluation is your own.