This article argues that for massive diffs you should let AI handle low-level checks and use your time to feed it the domain knowledge AI or the author lacks—like deprecated services, codebase conventions, or high-level design context. You prompt the AI with this “out-of-distribution” info instead of nitpicking every line, unless you’re in a context where each line is critical.

• For huge diffs, let AI catch syntax errors, style nits, and common vulnerability patterns instead of manually reading every hunk.
• The reviewer's real value is injecting "out-of-distribution" context the AI lacks—deprecated services, codebase conventions, design rationale—rather than nitpicking lines.
• This tradeoff only works for typical backend/web/mobile code; safety-critical or embedded systems still require full human line-by-line review.

Google has made its internal code review guides public on GitHub. One guide covers reviewers, another covers authors and defines terms like LGTM and CL. The repository shows how Google standardizes its code review process.

• Google published its internal code review guides (reviewer and author docs) publicly on GitHub under google/eng-practices.
• The guides mandate no CL sit unreviewed for more than 24 hours.
• Author guide specifies concrete conventions like "<area>: <summary>" commit messages and splitting big features into logical CLs.
• Reviewer guide gives specific checklists covering security, readability, test coverage, and performance (including C++ memory-allocation checks).