I have attempted to replicate the "workflow" LLM process where several LLMs come up with different variations of a way to solve a problem and a "judge" LLM reviews them and the go through different verification processes to see if this workflow increased the accuracy of the LLM's ability to solve the problem. For me, in my experiments, it didn't really make much difference but at the time I was using LLMs significantly dumber than current frontier models. HOWEVER...When I enable "Thinking Mode" on frontier LLM's like ChatGPT it DOES tend to solve problems that the non-thinking mode isn't able to solve so perhaps it's just a matter of throwing enough iterations at it for the LLM to be able to solve a particular complex problem.

> But how does one separate the good comments from the bad comments?

One thing that works very well for me (in a different context) is to ask to return two lists:

- Things that I must absolutely fix (bugs, typos, logic mistakes, etc.)

- Lesser fixes and other stylistic improvements

Then I look only at the first list.

You need human alignment on what constitutes a "good" comment. That means consistent rules.

Otherwise, some people feel review is too harsh, other people feel it is not harsh enough. AI does not fix inconsistent expectations.

> But how does one separate the good comments from the bad comments?

If the AI took a valid interpretation of the coding guidelines, it is a legitimate comment. If the AI is being overly pedantic, it is a documentation bug and we change the rules.