AI code review can speed up pull-request feedback, but treat its findings as suggestions—not proof that a change is correct or safe. Before connecting a private repository, check what the service can read, whether it retains review data or uses it to train models, what administrators can control, and whether its review can count as a required approval.
Is my code used to train AI models?
There is no category-wide answer: privacy terms vary by provider, plan, integration, and settings. Check the terms for the exact service and plan you intend to use. Also distinguish model training from data retention; a promise not to train on submitted code does not necessarily mean the service stores no review data.
For example, CodeRabbit’s privacy policy, updated December 10, 2025, says CodeRabbit and its named model providers do not use personal information collected as part of code review to train or refine models. The policy also describes optional storage—primarily vector embeddings—to improve reviews, with an opt-out. These statements describe CodeRabbit’s stated practices, not those of other AI reviewers. Read CodeRabbit’s privacy policy.
How much of my repository can an AI code reviewer access?
Do not assume a reviewer sees only the lines in a pull request. GitHub documents that Copilot code review’s agentic capabilities can gather context from the full project, and that reviews can use repository custom instructions, agent instructions, and relevant skills. The service’s access permissions and the feature’s actual review scope are separate questions.
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GitHub also says some file types—including dependency-management files, log files, and SVG files—are excluded from Copilot code review. An excluded file may still fall within the repository permissions granted to an integration; exclusion from review is not, by itself, evidence that the service cannot access it. Check the permissions requested during setup and the current product documentation for the feature and plan you use. See GitHub’s Copilot code review documentation.
Can I trust AI code review comments?
Use comments as leads to investigate, not as a substitute for human review, tests, or security analysis. An AI reviewer may miss a defect, misunderstand project intent, or suggest a change that introduces a problem. GitHub’s responsible-use guidance warns that Copilot output can appear valid while being syntactically or semantically incorrect or not reflecting the developer’s intent. It specifically urges care with Copilot Chat code for security-sensitive applications and says to review and test generated code thoroughly. That guidance concerns Copilot Chat’s generated code; it is not a measured accuracy score for every code-review product. Read GitHub’s responsible-use guidance.
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Be cautious with headline accuracy figures. CodeRabbit’s FAQ claims its product “catches 95%+ of bugs,” but the surfaced FAQ does not establish a test set, define “bug,” or provide a methodology or independent validation. Treat that figure as a vendor claim, not a general accuracy rate or a reliable comparison between products. See CodeRabbit’s FAQ.
Can an AI review approve a pull request or satisfy merge rules?
That depends on the product settings and repository policy. GitHub says Copilot’s default review is a comment, not an approval or a request for changes, so it does not count toward required approvals by default. Approval behavior can be configured; GitHub identifies approvals as a public preview, so availability and behavior may change. Administrators should verify the current settings rather than assume an AI review satisfies a branch-protection requirement. Check GitHub’s usage guide.
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GitHub’s guide also says a new push does not automatically trigger another review unless automatic reviews of new pushes are configured. A re-review may repeat comments that were previously resolved or downvoted, so teams should account for that behavior in their workflow.
What should I check before connecting a private repository?
- Confirm the exact product, plan, and integration. Read the applicable privacy and service terms; do not transfer one provider’s assurances to another.
- Inspect repository permissions. Identify which repositories and data the integration can access, and whether its review workflow gathers project context beyond the pull-request diff.
- Read retention and training terms separately. Look for what is stored, for how long, whether storage is optional, how deletion works, and whether submitted data may be used to train or refine models.
- Review administrative controls. Check who can enable automatic reviews, change review behavior, or configure approvals that may count toward merge requirements.
- Keep a human accountable. Verify proposed changes, run appropriate tests, and apply additional scrutiny to security-sensitive changes.
How should teams compare AI code reviewers?
Compare concrete controls and limitations rather than relying on a single accuracy claim.
Quick Recap
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| What to compare | Questions to ask |
|---|---|
| Repository scope | Which repositories, files, and related project context can the service access? Does it analyze only the diff or gather broader context? |
| Privacy terms | What data is retained, for how long, and for what purpose? Is it used for model training? Do the answers change by plan? |
| Administrative controls | Can administrators restrict access, configure automatic reviews, or control whether AI approvals count toward merge requirements? |
| Coverage limitations | Which file types or workflows are excluded, and are those exclusions distinct from the integration’s access permissions? |
| Accuracy evidence | Does a performance claim define its terms and publish a reproducible methodology or independent validation? |
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