Until that immediate feedback is outright wrong feedback and now you’ve sent them down a goose chase.

This is where prompting and context is key - you need to keep the scope of the review limited and well-defined. And ideally, you want to validate the review with another LLM before passing it to the dev.

Still won't be perfect, but you'll definitely get to a point where it's a net positive overall - especially with frontier models.

That happens with human review too and often serves as an opportunity to clarify your reasoning to both the reviewer and yourself. If the code is easily misunderstood then you should take a second look at it and do something to make it easier to understand. Sometimes that process even turns up a problem that isn’t a bug now but could become one later when the code is modified by someone in the future.