also any inputs are probably scraped

ftfy

Let’s hope it’s the bad outputs that are scrapped. <3

Eh, I’d say mostly.

I have one right now that looks at data and says “Hey, this is weird, here are related things that are different when this weird thing happened. Seems like that may be the cause.”

Which is pretty well within what they are good at, especially if you are doing the training yourself.

That sums up my experience too, but I have found it good for discussing functions for SQL and Powershell. Sometimes, it’ll throw something into its garbage code and I’ll be like “what does this do?” It’ll explain how it’s supposed to work, I’ll then work out its correct usage and solve my problem. Weirdly, it’s almost MORE helpful than if it just gave me functional code, because I have to learn how to properly use it rather than just copy/paste what it gives me.

All the while it gets further and further from the requirements. So you open five more conversations, give them the same prompt, and try pick which one is least wrong.

All the while realising you did this to save time but at this point coding from scratch would have been faster.

I interviewed someone who used AI (CoPilot, I think), and while it somewhat worked, it gave the wrong implementation of a basic algorithm. We pointed out the mistake, the developer fixed it (we had to provide the basic algorithm, which was fine), and then they refactored and AI spat out the same mistake, which the developer again didn’t notice.

AI is fine if you know what you’re doing and can correct the mistakes it makes (i.e. use it as fancy code completion), but you really do need to know what you’re doing. I recommend new developers avoid AI like the plague until they can use it to cut out the mundane stuff instead of filling in their knowledge gaps. It’ll do a decent job at certain prompts (i.e. generate me a function/class that…), but you’re going to need to go through line-by-line and make sure it’s actually doing the right thing. I find writing code to be much faster than reading and correcting code so I don’t bother w/ AI, but YMMV.

An area where it’s probably ideal is finding stuff in documentation. Some projects are huge and their search sucks, so being able to say, “find the docs for a function in library X that does…” I know what I want, I just may not remember the name or the module, and I certainly don’t remember the argument order.

Critical thinking is not being taught anymore.

I had a chat w/ my sibling about the future of various careers, and my argument was basically that I wouldn’t recommend CS to new students. There was a huge need for SW engineers a few years ago, so everyone and their dog seems to be jumping on the bandwagon, and the quality of the applicants I’ve had has been absolutely terrible. It used to be that you could land a decent SW job without having much skill (basically a pulse and a basic understanding of scripting), but I think that time has passed.

I absolutely think SW engineering is going to be a great career long-term, I just can’t encourage everyone to do it because the expectations for ability are going to go up as AI gets better. If you’re passionate about it, you’re going to ignore whatever I say anyway, and you’ll succeed. But if my recommendation changes your mind, then you probably aren’t passionate enough about it to succeed in a world where AI can write somewhat passable code and will keep getting (slowly) better.

I’m not worried at all about my job or anyone on my team, I’m worried for the next batch of CS grads who chatGPT’d their way through their degree. “Cs get degrees” isn’t going to land you a job anymore, passion about the subject matter will.

Altering the prompt will certainly give a different output, though. Ok, maybe “think about this problem for a moment” is a weird prompt; I see how it actually doesn’t make much sense.

However, including something along the lines of “think through the problem step-by-step” in the prompt really makes a difference, in my experience. The LLM will then, to a higher degree, include sections of “reasoning”, thereby arriving at an output that’s more correct or of higher quality.

This, to me, seems like a simple precursor to the way a model like the new o1 from OpenAI (partly) works; It “thinks” about the prompt behind the scenes, presenting only the resulting output and a hidden (by default) generated summary of the secret raw “thinking” to the user.

Of course, it’s unnecessary - maybe even stupid - to include nonsense or smalltalk in LLM prompts (unless it has proven to actually enhance the output you want), but since (some) LLMs happen to be lazy by design, telling them what to do (like reasoning) can definitely make a great difference.

It was ChatGPT from earlier this year. It wasn’t a huge deal for me that it made mistakes, because I had a very specific use case and just wanted to save some time; I knew I’d have to troubleshoot grafting it into my function, but even after I pointed out that it was using depreciated syntax (and how to correct it), it just spat out the code again with even more errors and still using depreciated syntax.

All LLMs will fail like this in some way, because they don’t actually understand what they’re generating (i.e. they have no mechanism for self-evaluating the veracity of their statements).