Effective Code Review Best Practices and Habits
Set clear standards for quality and pull request workflow so reviewers avoid arguing over style, letting automation handle formatting instead. Improve your reading skills by running the code locally and focusing on the specific purpose of the change rather than trying to learn the entire codebase. Communicate with the author, explain your reasoning when giving feedback, and document your testing steps and design choices in the pull request description.
How to Review Ai Generated Code Techniques
Treat AI-generated code like output from a junior developer, verifying the logic and watching for subtle errors instead of trusting it blindly. You remain responsible for any code you commit, so you must read, understand, and test it entirely. Compare the code against your original specification, since the AI might rationalize incorrect choices that checking the diff alone will miss. Ask the AI to generate pseudo-code to understand the overall flow before getting into the implementation details. Keep your task prompts small and focused rather than asking the AI to build an entire feature at once. Try running the code through multiple different AI models to cross-check their outputs and surface disagreements, and use traditional linters and type checkers before relying on AI review tools.