code-review-checklist

To run a consistent code review for security and quality, apply a checklist-driven assessment covering correctness, security, performance, testing, and documentation to identify gaps and improvements during pull requests.

Best practices for reviewing AI and LLM patterns involve using anti-pattern guidance to detect risky implementations. A dedicated code review checklist evaluates AI/LLM patterns alongside standard security and performance checks to ensure robust model integration.

Yes, you can use a code review checklist for both pull requests and merge requests. The assessment is designed for software development workflows, ensuring consistent evaluation across different peer review processes and team setups.

When evaluating code correctness in a peer review, look for logical errors and edge cases using comprehensive correctness checks. A structured code review checklist ensures you systematically validate behavior, security risks, and performance bottlenecks.

A checklist-driven code review does not slow down the software development workflow when used as guardrails for fast assessments. It streamlines identifying quality, security, and best-practice issues, enabling quick and consistent peer reviews.