How to Use AI for Code Review
A safe, practical workflow to review pull requests with AI: risks, edge cases, and test ideas.
Next Best Action
Finish this guide, then continue with another AI Coding tutorial to lock in the workflow.
FAQ Highlights
- Can AI replace human code review?
- What’s the best way to use AI if I can’t share code?
- How do I keep AI feedback actionable?
Introduction
AI can speed up code reviews, but only if you use it safely and ask for specific outputs (risks, edge cases, and tests). This guide shows a simple workflow you can use on any pull request.
Step 1: Share the right context (without sharing secrets)
Good AI review needs context:
- goal of the change
- affected modules
- expected behavior
- any constraints (performance, backwards compatibility)
Do not paste:
- API keys, tokens, passwords
- private customer data
- internal URLs that reveal sensitive systems
If you can’t share code, share a diff summary and the key functions involved.
Step 2: Ask for risks + edge cases (not a generic “looks good”)
Copy-paste prompt:
Act as a senior engineer doing a code review.
Goal of the change: [GOAL].
Language/framework: [STACK].
Review this diff and return:
1) Potential bugs or logic errors (bullets)
2) Edge cases we might miss (bullets)
3) Security/privacy risks (bullets)
4) Suggested tests (unit/integration) with specific scenarios
Be strict. If something is unknown, ask questions instead of guessing.
Diff:
[PASTE DIFF OR KEY FILES]
Step 3: Turn feedback into a small checklist for the author
The fastest reviews end with clear next actions:
- fix 1–3 high-risk items
- add 1–2 tests
- clarify 1 open question
Copy-paste prompt:
Convert the review notes below into a short author checklist.
Format:
- Must fix
- Should fix
- Tests to add
- Questions to answer
Review notes:
[PASTE NOTES]
FAQ
Can AI replace human code review?
No. It can accelerate the boring parts and suggest tests, but you still need a human to verify correctness and make risk tradeoffs.
What’s the best way to use AI if I can’t share code?
Share a structured summary: the PR goal, the key functions, and a pseudocode-level diff. Ask for risks and test ideas.
How do I keep AI feedback actionable?
Force specific output sections: bugs, edge cases, security, tests. Avoid open-ended prompts like “any thoughts?”.