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True. But it has no idea that it has no idea, so it might be able to look back at the session trace and pattern match its way to actionable feedback?

Listen friend I’m also in the camp of LLMs are just math and data and don’t actually “have an idea” about anything but that doesn’t make them any less accurate/effective at generating tokens

I’m not sure why this prompt is different than any other in that regard

If they try something and it doesn't work and they then try something else and it DOES work that's concrete feedback.

Are you saying the machine can tell you what confused them?

Yes. Try a prompt like this:

> In our recent conversations were there any instances where you attempted a tool call, got an error, then adjusted your tool call and were successful? Suggest improvements to the tool instructions that would have avoided the initial error.

A year ago I don't think this would have worked. The frontier models have been trained to be able to write prompts for themselves, mainly so they can effectively use sub-agents.

they're surprising good at this. The next tokens after a confusing string of tokens when asked to explain the confusing parts are pretty accurate!