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An AI code review proof of concept (POC) works when it answers a specific engineering question with evidence: does the tool surface useful issues in your pull requests without adding unacceptable noise, cost, or governance risk? Start with a small, representative set of repositories, capture how review works today, then test the AI reviewer alongside your existing checks and human review.
1. Define what the POC must decide
Choose one primary problem to investigate, such as long review waits or inconsistent first-pass checks. Avoid a vague goal like “use more AI.” State what success would look like and what trade-offs would make the tool unsuitable.
Limit the first evaluation to a small, representative set of repositories and a dedicated reviewer group. Include enough variation to reflect the languages and change types the team actually handles, but leave sensitive or production-critical repositories out if data handling, access controls, or operational readiness are unresolved. OpenAI’s Codex Security guidance recommends a small initial repository set and dedicated reviewer group; for teams not already using GitHub Cloud, it suggests lower-risk or non-production repositories for evaluation. Codex Security is an adjacent security-analysis product, not a requirement for an AI code review POC.
2. Record a baseline and agree on measures
Before enabling the AI reviewer, document the current process so you have a meaningful comparison. Record pull request volume, review and merge timing, existing defect and security checks, and how often reviewers request changes. Where possible, retain comparable pull requests and note their size, complexity, language, and staffing.
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Agree in advance on how reviewers will classify findings. A useful scheme is:
- Valid and actionable: the comment identifies a real issue and points to a practical fix.
- Duplicate: the issue is real but already identified by a human or another check.
- Irrelevant or false positive: the comment does not apply or is based on an incorrect assumption.
- Missed issue: a human or existing check finds an issue the AI reviewer did not flag.
- Needs domain judgment: validity depends on requirements or context that cannot be decided from the change alone.
Track adoption and engagement, suggestion acceptance or disposition, and pull request lifecycle measures such as pull request counts and median time to merge. GitHub describes these as ways to understand adoption and how AI-assisted workflows relate to throughput and cycle time; none alone demonstrates improved code quality or proves that the AI caused a workflow change. Add manual finding classification and review defect or security outcomes to understand whether the comments are actually useful. GitHub’s code review documentation describes its review metrics and workflow.
3. Confirm access, governance, and cost
Before inviting repositories into a pilot, confirm that the selected product is available on your plan and enabled by the organization. Decide which users and repositories may invoke it, and review the provider’s data handling, retention, permission, and administrative controls for your intended use.
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4. Configure repository context deliberately
Give the reviewer concise guidance about project conventions, review scope, and security checks that matter for the repository. For example, explain relevant architectural boundaries, prohibited patterns, or expectations for tests without asking the tool to restate generic style rules already enforced by automation.
GitHub documents repository instructions and relevant agent skills or MCP context for Copilot code review; it says the head branch’s instructions are used. Test the configuration on pilot pull requests and inspect whether the intended guidance was applied. Treat instructions as an input to evaluate, not a guarantee that the reviewer will follow every constraint. GitHub’s documentation explains repository context for Copilot reviews.
5. Run representative pull requests
Choose routine and more complex changes that cover the languages and change types in scope. Include cases where the team already knows what a good review should catch, but do not rely only on examples selected to make the tool look successful. Keep track of the review setting and any relevant repository instructions for each run.
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- Open the pull request you want to evaluate.
- In the pull request’s Reviewers section, request a review from Copilot.
- Record the effort setting used, such as Lite or Balanced, and save the resulting comments for classification.
Copilot code review is documented as available on paid Copilot plans, with availability also subject to organization policy. GitHub describes Lite as faster, targeted feedback for common issues and Balanced as using a higher-reasoning model for longer analysis; Balanced is documented as the default and costs more AI credits. Confirm the current availability and settings in GitHub’s Copilot code review documentation before implementation.
In GitHub Copilot’s default configuration, a review leaves a Comment review and does not count toward required approvals. Optional approval behavior is described as public preview in GitHub’s documentation. An AI comment is therefore not a merge approval. A quiet review is not evidence that the change is safe.
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6. Validate findings independently
Run existing automated tests and static analysis before interpreting AI feedback. GitHub’s tutorial states: “Always run automated tests and static analysis tools first.” The tutorial also cautions against accepting AI output simply because it looks right.
For each pull request, check the AI review against compilation, warnings, vulnerabilities, dependency issues, architecture and requirements, readability, maintainability, and licensing. Look for hallucinated APIs, incorrect logic, ignored constraints, deleted or skipped tests, and missed edge cases. Ask a human to review complex or sensitive changes. AI review should be a first-pass aid, not a replacement for tests, static analysis, or human judgment.
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A useful question for reviewers is: “What possible vulnerabilities or security issues could this code introduce?” Treat the answer as a prompt for investigation, not proof that a vulnerability exists or that the code is secure.
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7. Compare results and make a decision
Compare the pilot with the baseline using like-for-like pull requests where practical. Account for changes in size, complexity, language, and staffing; otherwise, a change in median time to merge or comment volume may reflect a different workload rather than the reviewer. Combine lifecycle measures with the classified findings and reviewer feedback.
If comparing tools or configurations, use matched changes where practical and assess:
- Finding validity and severity, including false positives and missed issues.
- Usefulness across languages, change types, and repository context.
- Integration friction and latency in the existing pull request workflow.
- Human reviewer time and pull request lifecycle measures.
- Data handling, permissions, and administrative controls.
- Total usage cost, including model credits and CI or Actions consumption.
Decide whether to continue, adjust, expand, or stop based on whether the tool finds useful issues without unacceptable noise, whether the workflow impact matters to the team, and whether controls remain effective. Treat observed changes as directional unless the POC design supports causal conclusions. Adoption or suggestion acceptance is not a proxy for software quality, and published product metrics are not independent evidence that a particular team’s productivity improved.
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