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.
>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?
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.