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Comment by HenriTEL | Hacker News Reader

That's far from my experience. Last time I used chatgpt it added many comments to explain the generated code and also attached rough explanatory text. You can also just ask more details about something, or to list other approaches, explain tradeoffs, etc. You can also ask for general questions to help you get started and find a good design.

To me it's about asking the right questions no matter the level of experience. If you're junior it will help you ramp up at a speed I could no imagine before.

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It's great doing things that has a tutorial or something out there, it falls flat when you try to do something slightly novel that doesn't have tons of articles on how to do it.

In my case, ChatGPT was quite useful to bootstrap a code for working with Apple's CoreAudio and sucked when I tried to make it write some code to do something special to my app.

I got the same experience when I tried to hammer out an app dealing with cryptography(certificates, keys etc) and was only able to solve the issue through Apple's developer forms thanks to to the infamous eskimo1. Then some Russian dev who had the same issues appeared and gave me the full context. ChatGPT, Claude etc. can't put together a Swift code that does things usually not done in Swift but done in C and can't leverage its knowledge on Cryptography to help itself out.

I had similar problems in nodeJS etc. too, the instance you do anything non-standart AI falls apart and you can see how stupid actually is.

> Last time I used chatgpt it added many comments to explain the generated code and also attached rough explanatory text.

Good way to know if a PR opened by someone on your team was LLM generated is to look for inane explanatory comments like

  // Import the necessary dependencies
  import { Foo } from './Foo';

  // Instantiate a new Foo
  const myFoo = new Foo();

  // return the expected value
  return true;

I don't know if this is because a lot of the training materials are blog posts and documentation, which are focused more on explaining how trivial cases work than "real world" examples, and thus the LLM has "learned" that this is what real code looks like.

I would not leave it to junior developers to ask the right questions.

In my experience, from a few juniors I've cooperated with, they get absolutely awful code from ChatGPT. Things like manual opening and closing of files with unnecessary exception handling that crudely reimplements stuff that's already in the standard library and should have been a one-liner, and sure, ChatGPT will happily suggest explanatory comments and whatnot, but it's like copying in material from a reference manual, i.e. provocatively useless.

To me it also seems like they don't learn much from it either. They've made much more progress from a bit of mentoring and heavy use of REPL:s and similar shells.

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