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You can review a code patch at zero cost with Git and the checks already in your project; AI can help surface possible defects, but its output is not proof that a change is correct. Start with a clearly defined diff, set a repeatable review checklist, run the project’s tests and static checks, then verify or reject every AI finding against the code.
How do I review AI-generated code?
Review the change, not the fact that AI wrote it. Establish which files and lines changed, identify what behavior should remain true, and inspect the full patch. Then combine human reading, project checks, and AI feedback as separate sources of evidence.
Git’s git-diff documentation covers comparisons between commits and between a commit and the working tree. Choose the comparison that matches your workflow: for example, a branch against its intended base, or uncommitted work against the current commit. No single diff range is correct for every repository.
The Tool Desk
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Before asking for review, identify the base version and the files included. Inspect the complete diff rather than relying on a summary. Keep unrelated formatting changes and generated artifacts out of the patch where practical, and do not paste secrets or sensitive data into a hosted review service.
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For an uncommitted working-tree change, git diff shows unstaged changes; staged changes can be inspected with git diff --cached. To compare a branch with its intended base, use the appropriate base and branch names for your repository, such as git diff BASE...BRANCH. Confirm the range before treating its output as the patch under review.
2. Set a review baseline
Write down the intended behavior, the parts of the system most exposed to risk, and the checks that should pass. A concise baseline gives both a human reviewer and an AI reviewer a more useful target than “check this code.” Include relevant context such as callers, data formats, permission boundaries, and failure handling where the diff alone does not make them clear.
3. Ask for concrete, change-caused defects
Ask the AI reviewer to focus on defects introduced by this patch, not general style preferences or broad rewrites. Request the changed line, a concrete failing scenario, and a distinction between supported findings and questions. The following is a reusable prompt, not a guarantee of review quality:
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Review only defects introduced by this patch. Focus on correctness, security, data loss, and behavior changes. For each possible issue, cite the changed line and explain a concrete failing scenario. Separate confirmed findings from questions. Do not praise style or suggest broad rewrites. If you find no supported defect, say so; do not infer that the patch is safe. I will run tests and verify each finding against the repository.
4. Run the repository’s checks
Run the tests, formatter or linter, type checks, and relevant security tooling that the project already provides. Use the project’s documented commands and record which checks passed, failed, or could not be run. An AI comment is not evidence that these checks ran, and AI review does not replace them.
5. Verify each finding and inspect for omissions
For every AI-reported issue, trace the changed code into relevant callers, tests, and surrounding behavior. Reproduce the claimed failure or establish why it follows from the code. Reject findings that are speculative, duplicate, or unrelated to the patch. Then inspect the patch yourself for risks the reviewer did not mention.
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6. Review the final diff and close the loop
After making fixes, inspect the new diff against the same intended base and rerun the checks affected by those edits. Record the baseline and results so the next patch can be reviewed consistently. A clean AI review means only that the tool reported no supported defect; it does not establish that the patch is safe.
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Yes, if “free” means using Git, human review, and existing project tooling without buying a separate service. That does not mean every hosted AI reviewer includes free code review. Plan entitlements, repository eligibility, usage limits, and automation settings vary, so check the current terms for the service and workflow you intend to use.
Git and project tooling: zero-cost baseline
Git diff inspection and checks already available in a repository can form a repeatable audit loop without a hosted AI feature. This option makes no claim about AI detection quality: the value is in defining the patch, running known checks, and applying human judgment consistently.
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GitHub Copilot: review is not included in Free
GitHub’s plans and pricing page says Copilot Free does not include code review. The page lists 2,000 completions and 50 chat requests for the Free plan; these are plan entitlements, not code-review credits or evidence of review accuracy, and plan details can change.
GitHub Docs state that each Copilot code review consumes AI credits, with the amount dependent on model and token volume. Organization use by people without a plan that includes code review can incur additional paid usage. Review automation and review-on-new-push behavior depend on configuration; a previous review does not mean every later push is automatically reviewed. See About GitHub Copilot code review and Using GitHub Copilot code review for current details.
Crashes, No Sound, or Screen Glitches?
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallCoverage also matters: GitHub documents file categories that can be excluded from review, including dependency-management files, logs, and SVGs. Do not infer that every changed file was examined just because a review comment appeared.
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CodeRabbit: public-repository and local options have conditions
CodeRabbit’s FAQ says reviews for public repositories are free and describes a free 14-day Team trial. A limited trial is not the same as ongoing free use. The FAQ also says the service is designed to complement, not replace, human review.
Its documentation describes a no-charge Pro tier for public repositories, with rate limits potentially applying, and a separate free VS Code extension that provides a subset of core features for local changes before a pull request. The documentation page says it was last updated August 15, 2025; verify current availability and terms before relying on those details.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What should you compare before choosing an AI review workflow?
“Free” alone does not tell you whether a tool fits the patch you need to inspect. Check the boundaries that affect cost, coverage, and your ability to verify results.
Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minute| Question | What to establish |
|---|---|
| What does free mean? | Distinguish a no-cost Git-and-project-tooling workflow, a free public-repository offer, a time-limited trial, included credits, and metered usage. Check the current plan and billing terms. |
| What does it review? | Determine whether it can examine local uncommitted changes, a pull-request diff, or broader repository context. |
| Which files are covered? | Check for excluded file types, repository restrictions, rate limits, and any other coverage limits; do not assume every changed file is included. |
| When does it run again? | Find out whether review is manual, automatic when a pull request is created, or configured to run on later pushes. |
| Can a person verify the result? | Keep the ability to inspect the cited code, reject unsupported findings, and run the repository’s own checks. |
How should you interpret a clean AI review?
Treat it as triage input, not approval. The official product information cited here describes features, plan rules, and usage conditions; it does not establish an independent defect-detection rate or prove that AI review improves code quality. A reviewer can miss an issue, misunderstand project behavior, or report a problem that the code does not contain.
The useful baseline is therefore broader than an AI prompt: a defined diff, expected behavior, repeatable checks, line-level verification of findings, and a human decision before the patch is accepted.
Quick Recap
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