GPT4 absolutely does the same thing if you ask it to. Tell it to suggest approaches and discuss pros and cons and future pitfalls and it does, which makes sense because it is in part stack overflow.

The silver lining of LLMs impact on Stack Overflow may be that the platform focuses on facilitating the deeper type of problem solving you describe. My experience with Stack Overflow over the years has been mixed. Frequently users misunderstand the question, threads take weeks or months to resolve, users are called incompetent by frustrated experts, answers are indecipherable to a non expert, and sometimes toxic. ChatGPT frequently outperforms these weakpoints.

If they embrace a chatGPT interface as a first line of defense for users questions, it may improve the quality of real questions, reduce repetition, and filter through actually good and compelling questions. LLMs still need systems that encourage high quality data generation from humans to improve its model. I would like to imagine a world where there is a win-win for AI and the website we've relied on so much over the years.

The worst is the large amount of questions that solve an unrelated problem that's specific to the single asker's use case. Question, how do you do x? Response, oh, you can y instead and avoid x. Sorry, I want to know how to x, change the question

Step 1: Microsoft buys Stack Overflow

Step 2: They implement GPT-4 responses to questions

Step 3: The community + context provide the same feedback

Step 4: GPT-5/6/7 gets progressively better

If they train GPT-4 to post wrong answers on the internet, and then learn from the responses, we'll have the singularity in a month.

This is partially down to good promoting though.

It has seen those Stack Overflow responses and if you ask it to give you more context about a certain approach, it will.

I've learned when asking GPT-4 for help to push past the first answer, or ask it to offer more solutions. Also to ask why it choose a certain approach.

Quite often it will give me a response that doesn't fit well with my code, and I'll say "I don't want to do it this way, please give me another option, keeping in mind that [some important aspect of the code base]."

It then spits it out and it's usually way closer. Iterate on this and it's often quite good.

I think you're completely right if a person were copy pasting the first response every time, but that person is simply using it wrong in my opinion.

>you realize that there's a wide range of solutions with some spectrum of a fit to your situation

I ran into this the other day and it's made me very leery about using chatGPT for learning things I don't already know. I moved on from toy examples and tried getting it to write a program in a domain I was already intimately familiar with: I immediately realized that this thing is going to help people churn out all sorts of code that is a poor fit for the surrounding system or ecosystem.

Basically I had it write a program in Elixir as a GenServer. After that was done I asked it how I could integrate that solution into a Phoenix web application. I had to hold chatGPT's hand, so to speak, as we evolved the solution from:

(1) Spawning (and linking!) the GenServer to a controller i.e. in response to a web request.

(2) Spawning and linking the GenServer to the application but outside the supervision tree.

(3) Spawning the GenServer inside a supervision tree.

Now technically the application could have worked, more or less, if you ran it at any one of those intermediate steps. However only the last solution is the robust and idiomatic one. It felt almost like a constraint solver to me: we iteratively arrived at the right answer by adding more and more constraints to the solution. The problem, as it relates to a novice, is that they don't have a list of constraints rattling around in their head. (Language idioms, best practices, framework knowledge, system architecture knowledge, etc.)

The insidious thing here is that 1 or 2 are syntactically valid programs. If you try to beat 1 and 2 into submission you're going to have a subtly broken system, one that makes a lot of Elixir programmers very sad.

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I'll be watching the Khan Academy guys pretty closely, because they seem to have put a lot of thought into how students could use these systems safely and effectively.[1] I think the answer is going to be we need AIs that are aimed at professionals, and AIs that are aimed at education. The hard part will be that somehow we need to direct people to use one appropriate to their skill level.

[1]: https://www.ted.com/talks/sal_khan_how_ai_could_save_not_des...