Copilot Code Review: when to click, when to keep a local checklist
AI review consumes quota or credits depending on your plan. It can help, but it does not replace your own checklist for business logic, security, and migrations.
Duration
9 min
Level
Intermediate
Tools
GitHub Copilot · Code review
An AI review creates a feeling of coverage.
It is not a guarantee. Copilot can find omissions, point out inconsistencies, and speed up a first pass. But the cost depends on your plan’s billing model, and the value depends on what you ask it to inspect.
Clicking without a strategy means paying for a generic checklist.
1. Start with your local checklist
Before the AI, write what only your team knows:
- which routes are sensitive;
- which files must not change without a migration;
- which auth or billing patterns are mandatory;
- which tests are real proof;
- which changes require senior human review.
Copilot can complement this checklist. It cannot invent it correctly from one PR.
2. Use AI for the first pass, not the decision
Good uses:
- spot an untested condition;
- flag a mismatch between description and diff;
- find dead imports;
- notice undocumented behavior changes.
Bad uses:
- approve a migration;
- validate permissions;
- judge a product decision;
- replace the owning reviewer.
Workflow: AI first to reduce noise, human second to decide.
3. Watch the real cost
GitHub has changed Copilot billing over time. Depending on your plan, your organization may be on an AI credits model or legacy premium request mechanisms. Legacy documentation also describes higher multipliers for some review actions.
Practical conclusion: do not assume AI review is free by default.
Define:
- which PRs deserve AI review;
- who can trigger automatic reviews;
- how usage is tracked;
- when a manual checklist is enough.
4. Short checklist
Before requesting AI review:
- the PR is under 500 lines when possible;
- the title describes the behavior changed;
- the description lists tests;
- sensitive files are named;
- CI is running.
After AI review:
- remove useless cosmetic comments;
- verify every finding against the code;
- request a targeted correction;
- never approve only because AI found nothing.
Sources
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