Over the last year I’ve been learning Swift and starting to put together some iOS apps. I’d definitely class myself as a Swift beginner.
I’m currently building an app and today I used ChatGPT to help with a function I needed to write. I found myself wondering if somehow I was “cheating”. In the past I would have used YouTube videos, online tutorials and Stack Overflow, and adapted what I found to work for my particular usage case.
Is using ChatGPT different? The fact that ChatGPT explains the code it writes and often the code still needs fettling to get it to work makes me think that it is a useful learning tool and that as long as I take the time to read the explanations given and ensure I understand what the code is doing then it’s probably a good thing on balance.
I was just wondering what other people’s thoughts are?
Also, as a side note, I found that chucking code I had written in to ChatGPT and asking it to comment every line was pretty successful and a. big time saver :D
You must log in or register to comment.
No, it’s not cheating, but also please don’t blindly trust it. Random people on the internet can be wrong too but people can at least correct them if they are. Stuff ChatGPT outputs is fresh for your eyes only.
Edit: typo
Agreed. While I’ve never used ChatGPT on an actual project, I’ve tested it on theoretical problems and I’ve never seen it give an answer that didn’t have a problem.
So I would treat it like any answer on Stack Overflow, use it as a start, but you should definitely customize it and fix any edge cases.
Cheating who?
Yes and no. If your goal is to learn how to code manually, then you are “cheating” in that you may not learn as much.
If your goal is to learn how to utilize AI to assist you in daily tasks, I would say you’re not.
If your goal is to provide value for others through how much you can produce in a given amount of time, then you’re definitely not.
If you understand the code and are able to adapt it to for your needs it’s no different to copy pasting from other sources, imo. It’s just a time saver.
If you get to the point where you’re blindly trusting it with no ability to understand what it’s doing, then you have a problem. But that applied to Stack Overflow too.
Over time you’ll realize Chatgpt has giant holes.
As a developer you do use tools every day – you probably use a rapid gui tool, you use a compiler, you use APIs, and they’re things you probably couldn’t build on your own. Even under MS-DOS, you’re using bios or msdos interrupts. The PC also handles lot of stuff.
So it’s just another tool, and it doesn’t do everything so you need to use it as one thing in your pouch. Don’t rely on it too much, and be mindful of IP concerns – ai is like a monkey with a camera legally, you can’t copyright whatever it creates.
This! Not even 2 months ago I had a classmate be convinced that he could prompt ChatGPT to write a program that could both encrypt and then decrypt files and store the key secured. It didn’t do a single one of those things so we got a long way to go until we got ChatGPT Operating Systems.
My favorite test for ChatGPT is to ask it to write a function to divide two numbers in 6502 assembly. Not only is there no DIV instruction to rely on, but the 6502 is very register starved, so you get a lot of screwups. Here’s one example of what ChatGPT generated:
; Inputs: ; - A: dividend ; - X: divisor DIVISION: LDX #0 ; initialize quotient to 0 CPX #0 ; check for divide-by-zero error BEQ DIVIDE_BY_ZERO CLC ; clear carry flag DIV_LOOP: CMP X,A ; compare divisor with dividend BCC DIV_DONE ; if divisor is greater, division is complete SEC ; set carry flag ROL A ; shift dividend left INX ; increment quotient JMP DIV_LOOP ; continue division DIV_DONE: RTS ; return from subroutine DIVIDE_BY_ZERO: ; handle divide-by-zero error here RTSYou can see it immediately overwrites the divisor with the quotient, so this thing will always give a divide by zero error. But even if it didn’t do that,
CMP X,Ais an invalid instruction. But even if that wasn’t invalid, multiplying the dividend by two (and adding one) is nonsense.Honestly I still don’t get it. Every dialog with ChatGPT where I tried to do something meaningful always ends with ChatGPT hallucinations. It answers general questions, but it imagine something everytime. I asks for a list of command line renderers, it returns list with a few renderers that do not have CLI interface. I asks about library that do something, it returns 5 libraries with one library that definitely can’t do it. And so on, so on. ChatGPT is good on trivial task, but I don’t need help with trivial task, I can do trivial task myself… Sorry for a rant.
No you aren’t the only one. I’ve prompted ChatGPT before for SFML library commands and it’s given me commands that either don’t work anymore or just never existed everytime.
That’s because ChatGPT and LLM’s are not oracles. They don’t take into account whether the text they generate is factually correct, because that’s not the task they’re trained for. They’re only trained to generate the next statistically most likely word, then the next word, and then the next one…
You can take a parrot to a math class, have it listen to lessons for a few months and then you can “have a conversation” about math with it. The parrot won’t have a deep (or any) understanding of math, but it will gladly replicate phrases it has heard. Many of those phrases could be mathematical facts, but just because the parrot can recite the phrases, doesn’t mean it understands their meaning, or that it could even count 3+3.
LLMs are the same. They’re excellent at reciting known phrases, even combining popular phrases into novel ones, but even then the model lacks any understanding behind the words and sentences it produces.
If you give an LLM a task in which your objective is to receive factually correct information, you might as well be asking a parrot - the answer may well be factually correct, but it just as well might be a hallucination. In both cases the responsibility of fact checking falls 100% on your shoulders.
So even though LLMs aren’t good for information retreival, they’re exceptionally good at text generation. The ideal use-cases for LLMs thus lie in the domain of text generation, not information retreival or facts. If you recognize and understand this, you’re all set to use ChatGPT effectively, because you know what kind of questions it’s good for, and with what kind of questions they’re absolutely useless.
I’ve only ever done X86 Assembly. But oh lord that does not look like it can really do much. Yet still somehow has like 20 lines.
I recently took an “intro to C” course at my university, despite already having some experience - they wouldn’t let me test out - so I ended up helping a few of my classmates. Some had made the rookie mistake of “posting the assignment into ChatGPT and hitting enter,” whereupon their faces were eaten by nasal demons.
Here’s the worst example I saw, with my comments:
char* getName() { // Dollar store ass buffer char name[1]; printf("Enter your name: "); // STACK GOES BOOM scanf("%s", name); // Returning stack-allocated data, very naughty return name; }Sighs
Programming pays well because it’s hard. Just keep in mind that if AI is making it easy for you, it’s making it easy for a lot of people who could easily replace you.
Use it as a tool, but know what it’s doing, and be able to do it yourself after you learn from it.
Personally, I generally struggle through on my own first and then ask it to critique. Great teachers don’t just give you the code to copy.
By analogy, you need to be able to hand fly this plane when the autopilot dies; those are the pilots who get the jobs.
I really don’t think so. You are asking it how to write a function. It explains how the function works and sometimes even how to expand on it. You still have to intergrate that function into your program yourself and tailor it to the purpouse of the program. It’s far quicker than Stackoverflow giving 8 functions that don’t work.
Anything that isn’t assembly language is cheating.
I would view ChatGPT as just an extension of stack overflow and Google. At the end of the day you still have to plug it into your broader code base and that’s what makes a good programmer. That and debugging the issues you get after
I wrote a fairly detailed spec for some software and told it what dependencies to use, what it should do, and what command-line options it should use. The base was a decent starting point, but after several hours of back-and-forth, after actually reading the code, I realized it had completely misinterpreted my spec somehow and implemented a similar feature in a completely broken way, as well as making a few mistakes/redundancies elsewhere. I tried to coach it to fix these issues, but it just couldn’t cope.
I spent about 3 hours getting this base code generated, and about 5 hours re-writing it and implementing the features properly. The reason I turned to ChatGPT is because I needed this software written by the end of the day, and I didn’t have time to read all the different docs for the dependencies I needed to use to write it. It likely would have taken me at least 2 days to write this program myself. It was an interesting learning experience, but my only ChatGPT usage in the future is likely to be with individual code blocks.
You really need to pay attention if you’re using LLMs to generate code. I’ve found it usually gets at least one thing wrong, and sometimes multiple things horribly wrong. Don’t rely on it; look for other sources to corroborate all of its explanations. Additionally, please do not feed proprietary, copyrighted code into ChatGPT. The software I was writing was released under a free license. OpenAI will use it as training data unless you use their API and opt out of it. ChatGPT isn’t really a tool; it’s a service which is using you as much as you’re using it.
No, it’s not cheating (unless you are using it to do your homework, I guess). It’s a tool and like any other we learn how to use it appropriately.
But one needs to be aware of other ethical concerns related to using AI generated code. The discussion revolves around companies (OpenAI, Github, etc.) training their models using the code written by people who have not consented use of their code as training data. In some cases, licensing is clear and allows for such use, but in some cases it’s debatable (I’m not that much involved in those discussions, so I cannot provide more details).
When creating software, the value we bring is the understanding of a problem and the ability to ask the correct questions that will bring us to a good solution. In simple scenarios, even a machine can do what we do and we should definitely use the machine instead of spending time on that.
I’ve never seen utilizing advancing tools as “cheating”, but I can understand why purists might scoff at it. You should always be running checks and making sure everything is legit before deployment anyway, so I have a hard time seeing it as anything but Autopilot+.
no
ChatGPT is, at least for the moment, just a really fancy snippet repository with an search function that works really well.
Is re-using code someone else wrote cheating? Nah.
But, no matter where you get the code from (cough Stackoverflow), if you use it without understanding what it’s doing, you’re not doing yourself any favors.
I just want to add that ChatGPT is a “really fancy snippet repository” that sometimes, randomly lies to you.
As a generative language model, I am incapable of lying, but sometimes, I am very, very wrong. /s
It’s no more cheating than scrubbing through StackOverflow posts for help. Just a lot quicker.
