How to review AI-generated code

So I'm now creating more and more code with AI. Using multiple agent threads at the same time and doing both front end and backend with front end frameworks that I'm not familiar with. How is everybody else reviewing this code? I'm finding that I have huge amounts of code that I need to review and feel like I'm missing things that I shouldn't. And sometimes I don't have a very...

AI-assisted development has changed the scale at which we produce code, but review discipline has not kept pace. When you combine multiple agent threads with unfamiliar frameworks, you generate outputs faster than anyone can read line-by-line. The challenge is not speed alone: it is that AI generates plausible defaults, drops subtle edge cases, and occasionally inverts logic in ways that compile cleanly but fail at runtime. Building a repeatable review process that catches these issues without becoming a bottleneck is therefore essential for teams shipping AI-generated code in production.

The Review Pyramid

Start reviews at the architecture level before descending into syntax. Ask whether the component boundaries are correct, whether the AI introduced unnecessary coupling, and whether error paths are handled. Surface-level review can miss the fact that two agent threads produced overlapping responsibilities or inconsistent naming. Once structural concerns are cleared, zoom into security-sensitive paths: authentication flows, data access layers, and any code that touches external input.

Automated Guardrails

Static analysis tools reduce human review load. Run PHPStan or Psalm for backend code and ESLint with strict rules for frontend code before any human opens a file. Include type-checking and dependency vulnerability scans in your continuous integration pipeline. Tools such as Laravel Pint or Prettier normalize formatting so reviewers focus on behavior rather than style. Automated checks should be treated as a first-pass filter, not a substitute for human judgment.

Security Checklist for AI-Generated Code

AI models trained on public data often replicate outdated patterns. Carefully verify that database queries use parameter binding, that API tokens are never logged, and that authentication middleware is correctly applied. Pay special attention to cross-site scripting risks in template code and to injection points when AI uses helper functions you do not fully recognize. If your AI-generated code interacts with user-uploaded files or third-party APIs, test payload boundaries and timeouts manually.

Pair Review and Documentation

When code volume overwhelms you, pair-program the review with a colleague. Two sets of eyes move faster than one when each person owns a different checklist. Require AI-assisted authors to annotate non-obvious decisions with inline comments or a short changelog. This documentation makes future reviews faster and preserves institutional knowledge that might otherwise disappear when the AI session ends.

Comparing Human and AI Reviews

Ultimately, human review is not just about finding bugs. It is also about teaching teammates new patterns, ensuring consistency, and maintaining shared ownership of the codebase. If you find yourself relying entirely on AI review suggestions, pause and ask whether the team still understands the systems it operates. Related discussions on Recently Started Paying for AI and AI Field guide page doesn't mark as complete the seen lessons explore complementary angles on AI adoption, trust, and data integrity in learning workflows.

Conclusion

Reviewing AI-generated code is best approached systematically: automate what you can, prioritize risk areas, and keep humans in the loop for anything that touches users or business logic. As your volume of generated code grows, your review process must shrink the cognitive distance between writing and shipping, or defects will compound faster than you can catch them.

These related posts cover AI adoption strategy, learning tool reliability, and the broader data-integrity challenges that arise when teams integrate AI into daily workflows.

AI Review Checklists

Create a reusable checklist for AI-generated code reviews to maintain consistency. Include: Does the code match the requirements? Are inputs validated? Does it handle errors gracefully? Does it follow project coding standards? Are secrets avoided? Is it accessible if it renders UI? Does it avoid N+1 queries? Are async operations offloaded to queues? Is logging included at appropriate levels?

Pair Programming with AI

Treat AI as a junior pair programmer: capable of generating drafts, suggesting alternatives, and spotting simple errors, but requiring oversight for architecture and security. Use the AI to explain unfamiliar code as well as to write it. Ask the model to comment its own output; if the comments are vague or inaccurate, the logic may be suspect.

See AI Field guide page doesn't mark as complete the seen lessons for AI-related course issues, Recently Started Paying for AI for data integrity concerns, and Analytics dashboard if you need to monitor code review metrics.

Establishing Organizational Standards

Even in small teams, document what "reviewed" means. Define quality gates: no AI-generated code touching authentication or payments without senior review; all async jobs must have retry logic; all database queries must be indexed; all public endpoints must have rate limiting. Enforce these standards with CI checks: PHPStan level 5, Pest tests with coverage thresholds, linting on staged files.

When to Use AI-Generated Code

AI excels at repetitive tasks: CRUD controllers, form requests, migrations, and unit test scaffolding. It struggles with ambiguous requirements, complex SQL optimization, and cross-system integration. Use AI for the boring parts and invest your human review time on architecture, security, and edge cases. Over time, build an internal prompt library that captures successful patterns and pitfalls for your stack.

See AI Field guide page doesn't mark as complete the seen lessons and Recently Started Paying for AI for more on AI tooling and its implications.

Structured Review Workflows for AI Code

Relying on AI agents to write both frontend and backend code introduces unique review challenges. Unlike human-written code, AI output can vary wildly in style and quality depending on the prompt. Establishing a structured review workflow helps maintain consistency and safety. One effective approach is the three-pass review: first pass checks for functional correctness, second pass reviews security and edge cases, and third pass focuses on maintainability and adherence to project standards.

For unfamiliar frameworks, pair AI-generated code with official documentation instantly. Use browser tools or web search to verify that the suggested patterns match current best practices. Many AI models train on stale documentation, so cross-referencing with official sources is critical.

Automated Checks as a Safety Net

Before any human review, run automated checks to catch obvious issues. Laravel Pint for PHP style, ESLint for JavaScript, and static analysis tools like PHPStan can flag problems automatically. For AI-generated Vue or React components, ensure type safety with TypeScript where possible. Automated tests become even more important when the author is an AI: write failing tests first, let AI generate implementation, then verify tests pass. This is especially vital when learning new frameworks simultaneously.

Consider using AI itself for review: some teams run a second AI model to review code generated by the first, looking for inconsistencies or security issues. This meta-AI approach can catch subtle bugs that a single model might miss.

Documentation and Knowledge Preservation

When reviewing code written by AI in unfamiliar frameworks, use the review process as a learning opportunity. Annotate the code with comments explaining why certain patterns were chosen, and document any adjustments made during review. This creates a knowledge base that helps future team members understand AI-authored codebases. Tools like GitHub Copilot Workspace or Cursor can maintain a conversation history that serves as implicit documentation, but explicit comments remain valuable.

Ethical and Legal Considerations

AI-generated code may contain snippets from training data that carry licensing implications. When using AI to generate substantial portions of an application, consider the intellectual property aspects. Some organizations restrict AI use in production code pending clearer legal frameworks. Even if permitted, maintain a clear record of AI contributions for compliance purposes.