AI Code Review for AI Generated Code
AI code review is useful when it removes work. It is harmful when it adds a second queue.
AI code review matters now because teams are drowning in AI generated code. The question is not "which bot talks the most." It is which check improves code quality without creating more triage.
Three layers
- Deterministic. Same findings every run. Fit for CI. aislop, linters, tests.
- Probabilistic. LLM PR review. Fit for suggestions, not as the only merge rule.
- Human. Intent, product risk, architecture. Still required.
Checklist: how to review AI written code. Gate: deterministic AI code review.
Measure the bot
Track unique issues that led to a fix, time spent dismissing comments, and defects that escaped to production. Comment volume is not code quality. Metrics post: AI code review metrics.
A starting command
npx aislop scan on the branch, then your existing reviewer. That order keeps AI slop out of the conversation the human still has to have.
Frequently asked questions
What is AI code review?
Any automated pass over a change: a deterministic scanner, an LLM pull request bot, or both. For AI generated code, start with a stable code quality gate, then add conversational review if it earns its comments.
How should I review AI generated code?
Prove behavior with tests, scan for AI slop, then inspect intent, security boundaries, and architecture. Do not read the diff top to bottom first.
Is AI based code review enough?
No. Model comments are hypotheses. Keep a deterministic gate and a human merge decision.