I don't know how to program, but to a very limited extent can sorta kinda almost understand the logic of very short and simplistic code that's been written for me by someone who can actually code. I tried to get to get chat GPT to write a shell script for me to work as part of an Apple shortcut. It has no idea. It was useless and ridiculously inconsistent and forgetful. It was the first and only time I used chat GPT. Not very impressed.

Given how it is smart enough to produce output that's kind of in the area of correct, albeit still wrong and logically flawed, I would guess it could eventually be carefully prodded into making one small snippet of something someone might call "good" but at that point I feel like that's much more an accident in the same way that someone who has memorised a lot of French vocabulary but never actually learned French might accidentally produce a coherent sentence once in a while by trying and failing 50 times before succeeding and then failing again immediately after without ever having even known.

For repetitive tasks, it can almost automatically get a first template you write by hand, and extrapolate with multiple variations.

Beyond that… not really. Anything beyond single line completion quickly devolves into either something messy, non working, or worse, working but not as intended. For extremely common cases it will work fine; but extremely common cases are either moved out in shared code, or take less time to write than to "generate" and check.

I've been using code completion/suggestion on the regular, and it had times where I was pleasantly surprised by what it produced, but even for these I had to look after it and fix some things. And while I can't quantify how often it happened, there are a lot of times where it's convincing gibberish.

Absolutely, but they need a lot of guidance. GitHub CoPilot often writes cleaner code than I do. I'll write the code and then ask it to clean it up for me and DRYify it.

The LLM can type the Code, but you need to know what you want / how you want to solve it.

Yes and no. GPT usually gives me clever solutions I wouldn’t have thought of. Very often GPT also screws up, and I need to fine tune variable names, function parameters and such.

I think the best thing about GPTis that it knows the documentation of every function, so I can ask technical questions. For example, can this function really handle dataframes, or will it internally convert the variable into a matrix and then spit out a dataframe as if nothing happened? Such conversions tend to screw up the data, which explains some strange errors I bump into. You could read all of the documentation to find out, or you could just ask GPT about it. Alternatively, you could show how badly the data got screwed up after a particular function, and GPT would tell that it’s because this function uses matrices internally, even though it looks like it works with dataframes.

I think of GPT as an assistant painter some famous artists had. The artist tells the assistant to paint the boring trees in the background and the rough shape of the main subject. Once that’s done, the artist can work on the fine details, sign the painting, send it to the local king and charge a thousand gold coins.

my dad uses this LLM python code generation quite routinely, he says the output's mostly fine.

Ai is excellent at completing low effort ai generated Pearson programming homework while I spend all the time I saved on real projects that actually matter. My hugging face model is probably trained on the same dataset as their bot. It gets it correct about half the time and another 25% of the time, I just have to change a few numbers or brackets around. It takes me longer to read the instructions than it takes the ai bot to spit out the correct answer.

None of it is "good" code but it enables me to have time to write good code somewhere else.

No. To specify exactly what you want the computer to do for you, you'd need some kind of logic-based language that both you and the computer mutually understand. Imagine if you had a spec you could reference to know what the key words and syntax in that language actually mean to the computer.

Writing code is probably one of the few things LLMs actually excell at. Few people want to program something nobody has ever done before. Most people are just reimplimenting the same things over and over with small modifications for their use case. If imports of generic code someone else wrote make up 90% of your project, what's the difference in getting an LLM to write 90% of your code?