Table of contents
- Why reviewing AI-generated code matters
- Key principles for reviewing AI-generated code
- Current tooling for reviewing AI-generated code
- Example workflow: using Graphite + static analysis
- Best practices summary
- Conclusion
- Frequently asked questions
As AI-generated code becomes more common, ensuring its quality and security is critical. This guide explores how to use AI tools—like Graphite and others—to review code generated by AI, with practical examples and sound engineering practices.
Why reviewing AI-generated code matters
AI-generated code can introduce technical debt when developers use the output without fully evaluating its impact, creating challenges in maintainability, performance, and reliability. Luckily, AI code review tools can speed up review and catch routine bugs, but human reviewers remain essential to interpret edge cases and context.
Key principles for reviewing AI-generated code
Treat AI output as first draft, not final code Always review generated code critically, applying the same rigor as human-written code.
Apply traditional engineering fundamentals Include architectural planning, testing, benchmarking, and performance/security audits—not shortcuts for speed.
Prioritize context-awareness Use tools that understand your entire codebase, not just isolated snippets.
Implement layered review Combine AI tools for initial feedback with human review for architecture, logic, and edge cases.
Focus on security, not just style or syntax AI can introduce vulnerabilities—so combine static analysis with security-focused review.
Keep human in the loop AI should support—not replace—human reviewers; maintain oversight and ownership.
Current tooling for reviewing AI-generated code
Here are some prominent tools you can integrate into your workflow:
Diamond Graphite offers AI-powered code review through Diamond, which analyzes pull requests with context-aware insights, custom rules, 1-click fixes, and immediate actionable comments.
Coderabbit AI-powered code review tool that provides automated feedback on pull requests with customizable rules and suggestions.
Cursor BugBot Focuses on detecting logic and security bugs, especially in AI-generated code, with GitHub integration.
GitHub Copilot Reviewer Integrated into the GitHub ecosystem, providing AI review capabilities directly within PR workflows.
Greptile Uses repo-wide context to detect syntax, logic, and style issues and offers senior-level suggestions.
Codacy Supports 30+ languages and integrates with GitHub, GitLab, and Bitbucket for automatic reviews.
Example workflow: using Graphite + static analysis
Generate code Ask AI (e.g., Claude Code or ChatGPT) to implement a function.
Initial cleanup Apply code formatter (e.g., Prettier, Black) for consistency.
Run AI review (Graphite Diamond) Connect PR to Graphite → receive comments on bugs, edge cases, performance, doc gaps → accept or refine suggestions.
Manual review and testing Ensure logic validity, edge case handling, performance profiling, maintainability.
Final testing & merge Ensure tests, CI, and human approvals are in place before merging.
Best practices summary
| practice | rationale |
|---|---|
| Leverage context-aware AI reviewers | Focuses feedback on code-base specifics |
| Combine AI review with static analysis | Covers security, style, complexity, and maintainability |
| Retain strong human oversight | Prevents AI-induced errors slipping through |
| Document AI-generated sections | Helps future maintainers understand origins |
| Train reviewers on AI quirks | Teaches them to watch for typical AI error patterns |
Conclusion
AI accelerates code generation—but it's just the start of the process. Using tools like Graphite, Coderabbit, Greptile, along with static analysis and human judgment, enables you to maintain code quality and safety. By following these practices and leveraging the FAQ as a quick reference, you'll strike a balance between innovation speed and engineering discipline.
Frequently asked questions
What is AI code review and how does it differ from a manual review?
AI code review uses LLMs to scan code for bugs, vulnerabilities, performance issues, and style deviations. It's faster and more consistent, but lacks the nuanced understanding of context and architecture that human reviewers bring.
Can AI tools replace human reviewers entirely?
No. AI tools are powerful assistants—but human expertise is still essential, especially for complex logic, domain knowledge, and architectural decisions.
How do I integrate AI code review tools into existing workflows?
Integration is generally straightforward. Most tools support webhooks or platform apps for GitHub, GitLab, Bitbucket, or CI pipelines. Just connect via the tool's app, configure rules or severity, and review feedback appears directly in PR comments.
Are there privacy or security concerns with using AI code review tools?
Yes—some tools process code remotely. Always evaluate provider security practices, check if on-premises or private model options are available, and ensure sensitive code isn't exposed unnecessarily.
What are common pitfalls to avoid when using AI code review?
Avoid focusing on trivial style issues instead of logic or maintainability; don't review changes in isolation—understand the broader system context; don't let large PR sizes overwhelm both AI and human feedback loops.
How do I measure success with AI code review?
Track metrics such as reduction in production bugs, review time, false positives from AI, developer satisfaction, and test coverage improvements. Also monitor acceptance vs. rejection rate of AI suggestions to fine-tune tool configuration.