[mrloba]

That depends on how many details I specify. If I specify a lot, I usually get what I want. But in the extreme this is just another form of coding (high quality code is quite similar to a detailed spec). In many cases I find I have to do many "passes" to get the right balance of correctness, performance, security, and clean architectural boundaries. Having a loop to fix these often makes it worse since they can often be contradictory.

There's also some types of code that I believe is often wrong in the training data that is almost always wrong in the LLM output as well. Typically anything that should have been a state machine, like auth flows, wizards, etc.

When all is said and done I think the main savings come from the high throughput of low-value generic solutions. I don't currently see this changing, and the reason is that high quality products cannot be generated without specifying a lot of details. Of course, we may not want quality.

[logicallee]

>There's also some types of code that I believe is often wrong in the training data that is almost always wrong in the LLM output as well. Typically anything that should have been a state machine, like auth flows, wizards, etc.

I'm curious about what you mean about state machines for auth. If it's a state machine, how do you interface with it, what data type do you use, how is it built? What language or framework would this apply to? I think your approach might be different from how other people do it, I'd like to know more about your approach. Could you walk through an example?