> lacking the context around how the change was produced, the plans, the prompting, to understand how an engineer came to this specific code change as a result. Did they one-shot this? did they still spend hours prompting/iterating/etc.? something in-between?

In my opinion, you have to review it the way you always review code. Does the code do what it's supposed to do? Does it do it in a clean, modular way? Does it have a lot of boilerplate that should be reduced via some helper functions, etc.

It doesn't matter how it was produced. A code review is supposed to be: "Here's this feature {description}" and then, you look at the code and see if it does the thing and does it well.

I mostly ignore how the code was made and review the artifact the same way I always have: correctness, tests, readability, and whether it matches the system design. The prompt history is fine, but it’s not something I want to rely on during review because it’s not stable or reproducible.

What does help is requiring a short design note in the PR explaining the intent, constraints, and alternatives considered. That gives the context reviewers actually need without turning the review into reading a chat transcript.

At the end of the day, the code is the only thing that matters. That is what defines, in concrete terms, what the program is doing. I don't view an LLM as a high level language, as we can run a prompt 10 different times and get 10 different results. This isn't valuable when it comes to programming.

Even without LLMs, there was a thought process that led to the engineer coming to a specific outcome for the code, maybe some conversations with other team members, discussions and thoughts about trade offs, alternatives, etc... all of this existed before.

Was that all included in the PR in the past? If so, the engineer would have to add it in, so they should still do so. If not, why do you all the sudden need it because of an AI involved?

I don't see why things would fundamentally change.