This specify-encode-fulfill loop/method is effective to make agents create bug-free code.
In my version of this workflow I do specify myself, then let the LLM do the rest.
This way 1.) I'm 100% sure the understanding/spec is good 2.) It's translated into an executable format so the implementation can be verified 3.) The implementation has maximum code coverage tests which steers the AI to produce code which follows standards, fits into the existing codebase, and it's very easy to refactor.
So far, this is the one and only advantage of using LLMs in my SWE practice. They glue together (human written) specs with code, with confidence, in no time.
In my version of this workflow I do specify myself, then let the LLM do the rest.
This way 1.) I'm 100% sure the understanding/spec is good 2.) It's translated into an executable format so the implementation can be verified 3.) The implementation has maximum code coverage tests which steers the AI to produce code which follows standards, fits into the existing codebase, and it's very easy to refactor.
So far, this is the one and only advantage of using LLMs in my SWE practice. They glue together (human written) specs with code, with confidence, in no time.