An AI code review proof of concept (POC) should answer a practical question: does the tool surface useful issues in your team’s pull requests without creating too much noise, cost, or governance risk? Start with a small, representative pilot, record a baseline, and classify every finding. Keep tests, static analysis, and human review in place throughout.
1. Decide what the pilot needs to prove
Choose a specific problem to evaluate, such as long waits for a first review or inconsistent checks for common defects. Define the decision you will make at the end: continue, change the configuration, expand to more repositories, or stop. A pilot without a decision criterion can produce activity without an answer.
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Start with a small set of repositories and a dedicated reviewer group. Exclude sensitive or production-critical repositories if data handling, access controls, or operational readiness are unresolved. OpenAI’s guidance for its adjacent Codex Security product also recommends a small initial repository set and dedicated reviewers, and suggests lower-risk or non-production repositories for evaluation when teams are not already using GitHub Cloud; those are product-specific recommendations, not requirements for every code review service. OpenAI’s Codex Security guidance
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Set a useful scope
- Include the languages and change types the team actually wants evaluated.
- Choose a reviewer group able to judge both routine findings and domain-specific advice.
- Keep the pilot narrow enough that people can inspect and classify each comment.
2. Record the baseline and define success measures
Before enabling AI review, capture the current workflow for the repositories and period you plan to evaluate. At minimum, record pull request volume, review and merge timing, existing defect and security checks, and how often human reviewers request changes. Agree on what qualifies as useful, actionable feedback and what counts as a false positive.
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Track measures that answer different questions rather than treating one metric as a verdict:
- Adoption and engagement: whether eligible pull requests receive AI review and whether reviewers inspect or act on the output.
- Finding disposition: whether comments are valid and actionable, duplicates, irrelevant, missed issues, or cases needing domain judgment.
- Workflow: pull request counts and median time to merge, compared with a suitable baseline.
- Quality and safety: defects, security issues, and test or static-analysis results found through the team’s existing checks and human review.
GitHub documents adoption, engagement, suggestion acceptance, and pull request lifecycle measures as ways to understand usage and workflow. They do not, by themselves, establish that code quality improved or that AI caused a change in throughput. GitHub’s code review documentation
3. Confirm access, cost, and data controls
Check that the chosen product is available on your organization’s plan, enabled by an administrator, and permitted for the intended repositories and users. Review the provider’s data handling and retention terms, permissions, and administrative controls before sending code through the service.
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For GitHub Copilot, code review is documented as available on paid Copilot plans, but organization policy can affect availability. Reviews consume AI credits and agentic capabilities may use GitHub Actions minutes. GitHub describes Lite as faster, targeted feedback for common issues and Balanced as a higher-reasoning option; Balanced is the documented default and uses more AI credits, potentially with marginally more Actions minutes. These product terms and settings can change, so verify current plan eligibility, effort options, and billing immediately before the pilot. GitHub’s code review documentation
4. Configure repository context deliberately
Give the reviewer concise project-specific instructions: conventions that matter, security checks to prioritize, and the review scope. Avoid vague direction that merely asks for a “thorough” review. GitHub documents repository instructions and relevant agent skills or MCP context for Copilot code review, and notes that it uses instructions from the pull request’s head branch. Test configuration changes on pilot pull requests and inspect whether the intended context was applied. GitHub’s code review documentation
Record which instruction set and effort setting were used for each evaluated pull request. Otherwise, a change in findings may reflect configuration differences rather than the tool’s performance.
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5. Run representative pull requests and classify findings
Include routine and more complex changes across the languages and change types in scope. Where practical, compare similar pull requests rather than drawing conclusions from a mix of unusually small and unusually risky changes.
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- Open the pull request on GitHub.
- In the pull request’s Reviewers section, request a review from Copilot.
- Select the available review effort setting, such as Lite or Balanced, and record it for evaluation.
- Review the resulting comments with a human reviewer and record a disposition for each: valid/actionable, duplicate, irrelevant or false positive, missed issue, or needs domain judgment.
In GitHub Copilot’s documented default configuration, a review leaves a Comment review and does not count toward required approvals. Optional approval behavior is described as public preview, so do not treat an AI comment as merge approval; confirm current behavior and organization settings in the official documentation. GitHub’s code review documentation
6. Validate findings independently
Run the team’s existing automated tests and static analysis before interpreting AI feedback. GitHub’s tutorial states: “Always run automated tests and static analysis tools first.” GitHub’s code review tutorial
Then check whether the review and the surrounding workflow catch problems in these areas:
- Compilation, warnings, tests, and edge cases.
- Vulnerabilities and dependency issues.
- Architecture, stated requirements, readability, and maintainability.
- Licensing concerns where relevant to the project.
- Hallucinated APIs, incorrect logic, ignored constraints, or deleted or skipped tests.
Ask a human to review complex or sensitive changes. A quiet AI review is not evidence that a change is safe, and a plausible-sounding comment is not proof that a defect exists. One useful review prompt is: “What possible vulnerabilities or security issues could this code introduce?”
7. Compare results and make the decision
Compare the pilot with the baseline using like-for-like pull requests where practical. Account for change size, complexity, language, and staffing. Consider whether comments were valid and useful, whether important issues were missed, how much reviewer time the workflow required, and whether pull request lifecycle measures changed. Pair usage and workflow metrics with manual finding classifications and the results of existing quality and security checks.
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Continue, adjust, expand, or stop based on whether the tool provides useful coverage without unacceptable noise, whether any workflow effect matters to the team, and whether governance controls remain effective. Treat observed changes as directional unless the pilot design supports a causal conclusion. Adoption or suggestion acceptance alone does not show improved software quality.
What to compare if you evaluate multiple configurations
Use the same practical criteria for each tool or setting, and keep the comparison grounded in the team’s workflow:
| Evaluation area | What to examine |
|---|---|
| Finding quality | Validity, severity, false positives, and issues the reviewer missed. |
| Coverage | Performance across the languages, change types, and repository context in scope. |
| Workflow fit | Integration friction, review latency, and human reviewer time. |
| Lifecycle | Pull request volume and median time to merge, interpreted alongside changes in work mix. |
| Governance | Data handling, permissions, and administrative controls. |
| Total usage cost | Model or AI credits and any CI or Actions consumption. |
Product availability, configuration, data handling, and cost vary by provider, plan, and organization. The GitHub workflow above is a documented example, not a universal setup or evidence that one provider outperforms another.
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