Ask HN: What happens to code review process when using LLMs?
As most people are, we are leaning more and more on LLMs to generate code. We use Cursor in the side-panel to maintain strict control and engineering quality standards of our application.
However, we are finding more and more that the code review is a tighter bottleneck in development, with PRs stacking up quickly. The balance of how long it takes to generate vs review code has skewed in the last year. Code review now takes proportionally more time.
We have tried integrating tools like CodeRabbitAI, but have not found it to be a silver bullet. It is useful for reporting basic bugs and missing test cases, but usually misses critical issues in the context of the wider application.
Additionally, AI code review misses the important code review goal of sharing the context and understanding and ownership with the rest of the team.
I'm concerned that we're ultimately looking at a bleak contradiction. We want both to generate new features quicker than can be traditionally code reviewed (i.e. - understood), and still want to personally understand the architecture and code that is being generated.
And so I'm looking for advice. What have you tried and found to be effective? What needs to be sacrificed in a professional engineering team generating code faster than ever before?