I must say, I do love how this comment has provoked such varying responses.

My own observations about using AI to write code is that it changes my position from that of an author to a reviewer. And I find code review to be a much more exhausting task than writing code in the first place, especially when you have to work out how and why the AI-generated code is structured the way it is.

▲

thegrim33 11 hours ago | parent | next [-]

There's a very wide range of programming tasks of differing difficulty that people are using / trying to use it for, and a very wide range of intelligence amongst the people that are using / trying to use it, and who are evaluating its results. Hence, different people have very different takes.

▲

seanmcdirmid 9 hours ago | parent | prev | next [-]

> especially when you have to work out how and why the AI-generated code is structured the way it is.

You could just ask it? Or you don’t trust the AI to answer you honestly?

▲

chmod775 7 hours ago | parent [-]

You're anthropomorphizing.

LLMs can't lie nor can they tell the truth. These concepts just don't apply to them.

They also cannot tell you what they were "thinking" when they wrote a piece of code. If you "ask" them what they were thinking, you just get a plausible response, not the "intention" that may or may not have existed in some abstract form in some layer when the system selected tokens*. That information is gone at that point and the LLM has no means to turn that information into something a human could understand anyways. They simply do not have what in a human might be called metacognition. For now. There's lots of ongoing experimental research in this direction though.

Chances are that when you ask an LLM about their output, you'll get the response of either someone who now recognized an issue with their work, or the likeness of someone who believes they did great work and is now defending it. Obviously this is based on the work itself being fed back through the context window, which will inform the response, and thus it may not be entirely useless, but... this is all very far removed from what a conscious being might explain about their thoughts.

The closest you can currently get to this is reading the "reasoning" tokens, though even those are just some selected system output that is then fed back to inform later output. There's nothing stopping the system from "reasoning" that it should say A, but then outputting B. Example: https://i.imgur.com/e8PX84Z.png

* One might say that the LLM itself always considers every possible token and assigns weights to them, so there wouldn't even be a single chain of thought in the first place. More like... every possible "thought" at the same time at varying intensities.

▲

pipes 6 hours ago | parent [-]

Or ask another model to tell you what the changes do.

	▲	cyclopeanutopia 2 hours ago \| parent \| next [-]
		And you could first read the thing to which you are replying. Don't tell me it was too long.
	▲	chmod775 6 hours ago \| parent \| prev \| next [-]
		Did you mean to reply to some other comment? I'm having trouble contextualizing your response - pardon the pun.
	▲	6 hours ago \| parent \| prev [-]
		[deleted]

▲

tayo42 8 hours ago | parent | prev [-]

Your always reviewing code though. Either a team mates pr or maybe your own code in 3 months, or some legacy thing.

	▲	christophilus 16 minutes ago \| parent [-]
		Human code is still easier to review. Also, I program 80% of the time and review PRs 20% of the time. With AI, that becomes: I review 80% of the time, and write markdown and wait 20% of the time.