Comment by nostrebored
5 months ago
This mirrors what I've seen. I've found that LLMs are most helpful in places where I have the most experience.
Maybe this is because of explicitness in prompt and preempting edge cases. Maybe it's because I know exactly what should be done. In these cases, I will still sometimes be surprised by a more complete answer then I was envisioning, a few edge cases that weren't front of mind.
But if I have _no_ idea things go wildly off course. I was doing some tricky frontend work with dynamically placed reactflow nodes and bezier curve edges. It took me easily 6 hours of bashing my head against the problem, and it was hard to stop using the assistant because of sunk cost. But I probably would have gotten more out of it and been faster if I'd just sat down and really broken down the problem for a few hours and then moved to implement.
The most tempting part of LLMs is letting them figure out design when you're in a time crunch. And the way it solves things when you understand the domain and the bottoms-up view of the work is deceptive in terms of capability.
And in this case, it's hoping that people on upwork understand their problems deeply. If they did, they probably wouldn't be posting on upwork. That's what they're trying to pay for.
I just had this conversation with a customer. And it’s hard to avoid anthropomorphizing ai. Once you equate the ai system with a human - a human who creates perfectly pep8 formatted python is probably a decent python programmer, whereas someone who bangs out some barely readable code with mixed spacing and variable naming styles is most likely a novice.
We use these signals to indicate how much we should trust the code - same with written text. Poorly constructed sentences? Gaps or pauses? Maybe that person isn’t as knowledgeable.
These shortcuts fail miserably on a system that generates perfect grammar, so when you bring your stereotypes gleaned from dealing with humans into the ai world, you’re in for an unpleasant surprise when you unpack the info and find it’s only about 75% correct, despite the impeccable grammar.
> But if I have _no_ idea things go wildly off course.
This is the key to getting some amount of productivity from LLMs in my experience, the ability to spot very quickly when they veer off course into fantasyland and nip it in the bud.
Then you point out the issue to them, they agree that they made a dumb mistake and fix it, then you ask them to build on what you just agreed to and they go and reintroduce the same issue they just agreed with you was an obvious problem... because ultimately they are more fancy auto complete machines than they are actual thinking machines.
I have found them to be a time saver on the whole even when working with new languages but I think this may in large part be helped by the fact that I have literally decades of coding experience that sets off my spidey senses as soon as they start going rampant.
I can't begin to imagine how comical it must be when someone who doesn't have a strong programming foundation just blindly trusts these things to produce useful code until the runtime or compile time bugs become unavoidably obvious.