AI Code Review

AI code review uses large language models to read a pull request and comment on it the way a senior engineer would, flagging likely bugs, security risks, and style problems directly on the changed lines before a human approves the merge.

What It Is

An AI reviewer ingests the diff, the surrounding code, and often the project’s conventions, then reasons about what the change does and where it might break. Instead of only matching patterns like a traditional linter, it can follow logic across functions, notice a missing null check, spot a leaked secret, or question an unclear API design. The output is a set of inline review comments rather than a pass or fail score.

In practice, AI code review sits inside the existing pull request workflow on platforms like GitHub and GitLab. It runs automatically when a PR opens, handles the repetitive first pass, and lets human reviewers concentrate on architecture, trade-offs, and intent. It does not replace human judgment, because models can miss context, raise false positives, or misjudge a deliberate decision.

One Sentence to Remember

AI code review is an automated first-pass reviewer that catches routine defects on every pull request, so humans can spend their attention on the decisions that actually need judgment.