Let's be real. Most of the time you ask an LLM "Why did you do it like this?", it responds with something along the lines of "Oops. My bad. You're right to point this out."

You even have a fair chance of getting a response like that when there isn't anything wrong and the question wasn't rhetorical - which perfectly illustrates the level of the genuine understanding LLMs operate at.

When you criticize AI, always remember that the alternative is the average employee. Today's models are pretty good.

I remember hearing (perhaps last year?) that the model companies have specifically tried to obfuscate the "thinking/reasoning" behind the decisions the models make so as to prevent cheaper models from training on the reasoning logs. So asking one "why did you do it like this" might be not fruitful.

Not sure if that's true or if it might be influencing what you're seeing, but it's a thought.

This has happened to me, so I put this in my global CLAUDE.md, and it seems to help (I don't remember getting the response you mentioned for awhile now):

    **Lead with the answer when asked how/which/whether.** Name the command/mechanism first; a question seeking understanding isn't a go-ahead to execute. Answer, then offer to act.

That's because of a fundamental misunderstanding of what an LLM is. The only correct answer to "Why did you do it like this?" is that the specific combination of input text and RNG state caused this particular output. There's no reasoning to be had.

* EDIT * What's with the downvoting? That's a correct description of what happened. You can't ask an LLM why it did something and expect a coherent response, because there's no thinking chain, and no stored thinking state... At best, you can get a reconstruction of how the context relates to the output (basically a summarization of the context).

Can't remember the last time that happened.