[altmanaltman]

Honestly, I fundamentally disagree with this. Figuring out "what to prompt" is not problem-solving in a true sense imo. And if you're really going too deep into the problem domain, what is the point of having the code abstracted?

My comment was based on you saying you don't care about the code and only what it does. But now you're saying you care about the code and review everything so I'm not sure what to make out of it. And again, I fundamentally disagree that reviewing code will become optional or rather should become optional. But that's my personal take.

[sho]

> Figuring out "what to prompt" is not problem-solving in a true sense

This just sounds like "no true scotsman" to me. You have a problem and a toolkit. If you successfully solve the problem, and the solution is good enough, then you are a problem solver by any definition worth a damn.

The magic and the satisfaction of good prompting is getting to that "good enough", especially architecturally. But when you get good at it - boy, you can code rings around other people or even entire teams. Tell me how that wouldn't be satisfying!

[keeda]

> My comment was based on you saying you don't care about the code and only what it does. But now you're saying you care about the code and review everything so I'm not sure what to make out of it.

I'm not the person you originally replied to, so my take is different, which explains your confusion :-)

However I do increasingly get the niggling sense I'm reviewing code out of habit rather than any specific benefit because I so rarely find something to change...

> And if you're really going too deep into the problem domain, what is the point of having the code abstracted?

Let's take my current work as an example: I'm doing stuff with computer vision (good old-fashioned OpenCV, because ML would be overkill for my case.) So the problem domain is now images and perception and retrieval, which is what I am learning and exploring. The actual code itself does not matter as much the high-level approach and the component algorithms and data structures -- none of which are individually novel BTW, but I believe I'm the only one combining them this way.

As an example, I give a high-level prompt like "Write a method that accepts a list of bounding boxes, find all overlapping ones, choose the ones with substantial overlap and consolidate them into a single box, and return all consolidated boxes. Write tests for this method." The AI runs off and generates dozens of lines of code -- including a tunable parameter to control "substantial overlap", set to a reasonable default -- the tests pass, and when I plug in the method, 99.9% of the times the code works as expected. And because this is vision-based I can immediately verify by sight if the approach works!

To me, the valuable part was coming up with that whole approach based on bounding boxes, which led to that prompt. The actual code in itself is not interesting because it is not a difficult problem, just a cumbersome one to handcode.

To solve the overall problem I have to combine a large number of such sub-problems, so the leverage that AI gives me is enormous.

[skydhash]

What people are wary of is not solving the problem in the first pass. They are wary of technical debt and unmaintainable code. The cost of change can be enormous. Software engineering is mostly about solving current problems and laying the foundation to adapt for future ones at the same time. Your approach's only focus is current problems which is pretty much the same as people that copypaste from StackOverflow without understanding.

[keeda]

Technical debt and understanding is exactly why I still review the code.

But as I said, it's getting rare that I need to change anything the AI generates. That's partly because I decompose the problem into small, self-contained tasks that are largely orthogonal and easily tested -- mostly a functional programming style. There's very little that can go wrong because there is little ambiguity in the requirements, which is why a 3 line prompt can reliably turn into dozens of lines of working, tested code.

The main code I deal with manually is the glue that composes these units to solve the larger computer vision problem. Ironically, THAT is where the tech debt is, primarily because I'm experimenting with combinations of dozens of different techniques and tweaks to see what works best. If I knew what was going to work, I'd just prompt the AI to write it for me! ;-)