Choose one question for the pilot
An AI review pilot should answer a concrete question about your team's work. For example: does this reviewer produce useful, verifiable findings on small backend pull requests without adding unacceptable investigation work? That is easier to evaluate than a broad claim that AI will improve engineering.
Choose a limited repository set and an explicit review period. Two weeks can be a useful planning window for a small trial, but it is not a promise of enough evidence for every team. If few representative changes arrive, extend the observation period rather than drawing a conclusion from a single impressive result.
Agree on the data boundary first
Confirm that the selected repositories may use the intended review method. AntiCode's managed AI reviews send the selected pull-request diff through the hosted service to OpenAI. Local deterministic checks are a different workflow. Make the distinction clear to everyone participating so they can choose an appropriate repository and avoid including unnecessary sensitive material.
Start by signing in with GitHub, connecting the repository, and syncing a focused pull request. Customers do not need a personal OpenAI key for the managed workflow. Keep the normal human review and merge process in place while you evaluate the tool; a pilot should not silently transfer approval authority.
Include defects and safe changes
A sample containing only known bugs tests whether a reviewer can find something, but not whether it will invent problems in reasonable code. Include a few confirmed historical defects, their corrected versions, and ordinary changes that a human considers acceptable. Use synthetic examples when real code is inappropriate to share.
Keep the expected outcomes outside the prompt used for the test. Otherwise, you may be measuring how well the model repeats the answer. OpenAI's evaluation guidance recommends task-specific tests and human calibration. For code review, that means judging the actual claim and its evidence, not merely checking whether the response contains a desired keyword.
Use a small, explicit scorecard
Ask a reviewer familiar with the code to assess each finding. Record whether the reference is valid, whether the failure is real, whether the explanation is actionable, and how much work was needed to decide. Separately record known defects that the model missed. A tool can produce convincing findings while still overlooking the issue that mattered most.
Use the same definitions throughout the trial. If reviewers disagree, preserve the disagreement and resolve it with code or requirements rather than averaging it away. Personal triage in AntiCode can organize an individual's decisions, but keep the shared pilot scorecard in your normal collaboration system.
- Valid reference: the cited code belongs to the reviewed revision.
- Correct reasoning: the stated trigger produces the claimed problem.
- Useful action: the finding gives enough context to investigate or fix.
- Investigation cost: the team records the work required to assess it.
- Missed issue: a known relevant defect was not identified.
Decide what to expand, change, or stop
At the end of the pilot, inspect the examples behind the scores. Identify which kinds of change benefited and which produced noise. A narrow success may justify expanding to similar repositories, while a recurring failure may suggest a smaller scope or another review method. Do not turn a promising demonstration into a universal benchmark.
Write down the decision, the evidence, and the conditions for revisiting it. Keep a small regression set as the service, model, or workflow changes. AntiCode reviews remain manual and require human judgment; the useful outcome of the pilot is a clearer place for that assistance in your process, supported by observations your team can explain.
Further reading
Put it into practice
Start with a small change you are authorized to review. Follow the AntiCode setup guide, inspect each finding, and keep your tests and human approval in the loop.