code-review-checklist

A code review checklist for AI and LLM development should cover correctness, security, performance, code quality, testing, and documentation, alongside specific checks for AI logic, hallucinations, prompt engineering safety, and output sanitization.

Perform a structured code review by systematically evaluating the pull request against detailed checklists for security, performance, and code quality, while using a standardized comment guide to leave blocking issues, suggestions, nits, or questions.

Common anti-patterns identified during code reviews highlight recurring coding practices that violate best practices in correctness, security, performance, and documentation, which reviewers can flag to ensure adherence to quality standards.

Yes, you can use a standardized comment guide to facilitate clear communication by categorizing your code review feedback into blocking issues, suggestions, nits, and questions for the developer.

The best way to review prompt engineering safety is to apply specific code review checklist guidelines for AI and LLM development, verifying output sanitization and checking for potential AI hallucinations and vulnerabilities.

Yes, the checklist works for general software engineering tasks by providing comprehensive review standards for correctness, security, performance, code quality, testing, and documentation, while offering additional specialized guidelines for AI projects.