It's not really "all this work," once you have good prompts you can use them to crank out a lot of code very quickly. You can use it to crank out thousands of lines of code a day that are somewhat formulaic, but not so formulaic that a simple rules based system could do it.
For example, I took text document with headers for table names and unordered lists for table columns, and had it produce a database schema which only required minor tuning, which I then used to generate sqlmodel classes and typescript types. Then I created an example component for one entity and it created similar components for the others in the schema. LLMS are exceptionally good at this sort of domain transformation, a decent engineer could easily crank out 2-5k lines/day if they were mostly doing this sort of work.
Now your description of "good prompts" to reuse has created an abomination in my mind. I blame you.
The abomination: prompts being reused by way of yaml templating, a Helm chart of sorts but for LLM prompts. The delicious combination of yaml programming and prompt engineering. I hope it never exists.
You know with GPT you can do these steps in a language you are not familiar with and it will still work. If you don't know some aspect of the language or it's environmental specifics you can just chat until you find out enough to continue.
Is GPT actually saving you any time if it can't actually do the hard part?