That is a smart approach. Cross-reviewing AI-generated code with multiple models seems like a practical way to reduce blind spots and catch different kinds of issues.
I also liked your point that human judgment is still essential, especially in deciding when more review stops adding value.
Would you be open to sharing your actual workflow or template for this? For example, how you structure the review cycle, what you ask each model to check, and what signals tell you it is time to stop iterating.
I think that would be really useful for people trying to turn this into a repeatable process instead of doing it ad hoc.
That is a smart approach. Cross-reviewing AI-generated code with multiple models seems like a practical way to reduce blind spots and catch different kinds of issues.
I also liked your point that human judgment is still essential, especially in deciding when more review stops adding value.
Would you be open to sharing your actual workflow or template for this? For example, how you structure the review cycle, what you ask each model to check, and what signals tell you it is time to stop iterating.
I think that would be really useful for people trying to turn this into a repeatable process instead of doing it ad hoc.
pretty straightforward-- here is 1 cycle:
Github Repo
Thanks for sharing this.
I will check it out. This seems interesting