There is a well-loved debugging technique with a silly name: rubber-duck debugging, where you explain your problem, line by line, to an inanimate rubber duck. It works far more often than it has any right to, and understanding why reveals something about problem-solving — and about how AI assistants can help even when they say nothing useful.
The magic is not in the duck. It is in the explaining.
Why explaining works
To explain a problem clearly, you must slow down and articulate each step and assumption, and that act of forcing your vague mental model into explicit words frequently exposes the flaw. The moment you say "and then this should be true..." you often notice that it is not. The solution appears not because the duck responded, but because you were made to think carefully.
Confusion thrives in the gaps of a fuzzy mental model; explanation drags those gaps into the light.
The AI as a very patient duck
An AI assistant makes an excellent rubber duck, with a bonus: it can also respond. Writing out your problem to ask it forces the same clarifying articulation, and you will sometimes solve the issue in the act of describing it, before ever reading a reply. When you do read a reply, it may add a useful angle.
Either way, the discipline of stating the problem fully is doing much of the work — the assistant just gives that discipline a destination.
Making it a habit
The practical lesson is to reach for explanation whenever you are stuck: describe the problem, your expectations, and what actually happens, in plain words, to a colleague, a duck, an assistant, or even a comment you write and delete. The goal is to externalise your thinking so its flaws become visible.
It costs nothing and resolves a surprising share of bugs before help even arrives. When stuck, do not just stare — explain.