A Student's Guide to Writing with ChatGPT

A current 3rd year college student here. I really want LLMs to help me in learning but the success rate is 0.

They often can not generate relatively trivial code When they do, they can not explain that code. For example, I was trying to learn socket programing in C. Claude generated the code, but when I stared asking about stuff, it regressed hard. Also, often the code is more complex than it needs to be. When learning a topic, I want that topic, not the most common relevant code with all the spagheti used on github.

For other subjects, like dbms, computer network, when asking about concepts, you better double check, because they still make stuff up. I asked ChatGPT to solve prev year question for dbms, and it gave a long, answer which looked good on surface. But when I actually read through because I need to understand what it is doing, there were glaring flaws. When I point them out, it makes other mistakes.

So, LLMs struggle to generate concise to the point code. They can not explain that code. They regularly make stuff up. This is after trying Claude, ChatGPT and Gemini with their paid versions in various capacities.

My bottom line is, I should NEVER use a LLM to learn. There is no fine line here. I have tried again and again because tech bros keep preaching about sparks of AGI, making startup with 0 coding skills. They are either fools or genius.

LLMs are useful strictly if you already know what you are doing. That's when your productivity gains are achieved.

Care to share any of these chats?

I'm starting to suspect that people generally have poor experiences with LLMs due to bad prompting skills. I would need to see your chats with it in order to know if you're telling the truth.

The simpler explanation is that LLMs are not very good.

I can get an LLM to do almost anything I want. Sometimes I need to add a lot of context. Sometimes I need to completely rewrite the prompt after realizing I wasn't communicating clearly. I almost always have to ask it to explain it's reasoning. You can't treat an LLM like a computer. You have to treat it like a weird brain.

The point is, your position is against a inherent characteristic of LLMs.

LLMs hallucinate.

That's true and by how they are made it cannot be false.

Anything they generate cannot bw trusted and have to be verified.

They are good at generating fluff but i wouldn't rely on them for anything.

Ask at that temperature glass melts and you will get 5 different answers, noone true.

It got the question correct in 3 trials, with 1 of the trials being the smaller model.

GPT4o

https://chatgpt.com/share/673578e7-e34c-8006-94e5-7e456aca6f...

GPT4o

https://chatgpt.com/share/67357941-0418-8006-a368-7fe8975fbd...

GPT4o-mini

https://chatgpt.com/share/673579b1-00e4-8006-95f1-6bc95b638d...

The problem with these answers is that they are right but misleading in a way.

Glass is not a pure element so that temperature is the "production temperature" but as an amorphous material it ""melts"" in the way a plastic material ""melts"" and can be worked at temperature as low as 5-700c.

I feel like without a specification the answer is wrong by omission.

What "melts" means when you are not working with a pure element is pretty messy.

This came out in a discussion for a project with a friend too obsessed with GPT (we needed that second temperature and i was "this can't be right....it's too high")

There is no easy way to share. I copied them in google docs: https://docs.google.com/document/d/1GidKFVgySgLUGlcDSnNMfMIu...

One with ChatGPT about dbms questions and one with claude about socket programming.

Looking back are some questions a little stupid ? Yes. But affcourse they are! I am coming with zero knowledge trying to learn how the socket programming is happening here ? Which functions are begin pulled from which header files, etc.

In the end I just followed along with a random youtube video. When you say, you can get LLM to do anything, I agree. Now that I know how socket programming is happening, for next question in assignment about writing code for crc with socket programming, I asked it to generate code for socket programming, made the necessary changes, asked it generate seperate function for crc, integrated it manually and voila, assignment done.

But this is the execution phase, when I have the domain knowledge. During learning when the user asks stupid questions and the LLM's answer keep getting stupider, then using them is not practical.

Yes. This is funny when I know what is happening and I can "guide" the LLM to the right answer. I feel that is the only correct way to use LLMs and it is very productive. However, for learning, I don't know how anyone can rely on them when we know this happens.

is english not your first language?

Also Im surprised you even got a usable answer from your first question asking for a socket program if all you asked was the bold part. I'm a human (pretty sure at least) and had no idea how to answer the first bold question.

No, english is my second language.

I had already established from previous chat that upon asking for server.c file, llm's answer was working correctly. Rest of the sentence is just me asking it to use and not use certain header files which it uses by default when you ask it to generate server.c file.Thats because from docs of <sys/socket.h>, I thought it had all relevant bindings for the socket programming to work correctly.

I would say, the sentence logically makes sense.

I mean. Likely, yes, but if you have to spend the time to prompt correctly, I'd rather just spend that time learning the material I actually want to learn

You're not exactly selling it as a learning tool with this comment.

If the premise is that you first need to learn an alien psychology, that's quite the barrier for a student.

Brace yourself, people who are going to come to tell you that it was all your fault are here!

I got bullied at a conference (I was in the audience) because when the speaker asked me, I said AI is useless for my job.

My suspicion is that these kind of people basically just write very simple things over and over and they have 0 knowledge of theory or how computers work. Also their code is probably garbage but it sort-of works for the most common cases and they think that's completely normal for code.

I had no idea what even the question was. I had chatgpt (4o) explain it to me, and solve it. I now know what candidate keys are, and that the question asks for AB and BC. I'd share the link, but chatgpt doesn't support sharing logs with images.

So you did not convince me that LLMs are not working (on the contrary), but I did learn something today! thanks for that.

I've been programming for 20 years and mostly JS for the last 10 years. Right now, I'm learning Go. I wrote a simple CLI tool to get data from several servers. Asked GPT-4o to generate some code, which worked fine at first. Then I asked it to rewrite the code with channels to make it async and it contained at least one major bug.

I don't dismiss it as completely useless, because it pointed me in the correct direction a couple times, but you have to double-check everything. In a way, it might help me learn stuff, because I have to read its output critically. From my perspective, the success rate is a bit above 0, but it's nowhere close to "magical" at all.

I was talking about coding in this context. With coding, you need to communicate a lot better than if you're just asking it to explain a concept.