TIL: you could add a ".diff" to a PR URL. Thanks!

As for PR reviews, assuming you've got linting and static analysis out the way, you'd need to enter a sufficiently reasonable prompt to truly catch problems or surface reviews that match your standard and not generic AI comments.

My company uses some automatic AI PR review bots, and they annoy me more than they help. Lots of useless comments

I would just put a PR_REVIEW.md file in the repo an have a CI agent run it on the diff/repo and decide pass or reject. In this file there are rules the code must be evaluated against. It could be project level policy, you just put your constraints you cannot check by code testing. Of course any constraint that can be a code test, better be a code test.

My experience is you can trust any code that is well tested, human or AI generated. And you cannot trust any code that is not well tested (what I call "vibe tested"). But some constraints need to be in natural language, and for that you need a LLM to review the PRs. This combination of code tests and LLM review should be able to ensure reliable AI coding. If it does not, iterate on your PR rules and on tests.

`gh pr diff num` is an alternative if you have the repo checked out. One can then pipe the output to one's favorite llm CLI and create a shell alias with a default review prompt.

> My company uses some automatic AI PR review bots, and they annoy me more than they help. Lots of useless comments

One way to make them more useful is to ask to list the topN problems found in the change set.

> TIL: you could add a ".diff" to a PR URL. Thanks!

You can also append ".patch" and get a more useful output