Review AI-assisted pull requests while building engineering skills

An AI-assisted pull request should give its reviewer enough information to understand the change and assess its behavior. For a junior engineer, that review can also become a structured learning exercise. The process below is a proposed team practice, not evidence about hiring trends, measured productivity or the performance of a particular team.

Agree on the task before generating code

Write a short acceptance statement: which behavior should change, which inputs are valid and which existing behavior must remain. Keep the first change small enough to explain and review. Ask the author to identify assumptions that are still uncertain rather than concealing them behind a polished implementation.

Choose a mentor or reviewer with relevant system knowledge. The junior engineer should participate in explaining and testing the code, but should not become the sole owner of security or architectural decisions beyond their experience. Make escalation part of the ordinary review process.

Trace the code instead of trusting its fluency

GitHub's responsible-use guidance for Copilot inline suggestions advises reviewing and testing generated code and describes limitations that can produce inaccurate or insecure suggestions. Treat an AI explanation as something to check against the implementation and documentation, not as an independent review of its own output.

Ask the author to trace one successful request and one failure through the changed code. Identify where data enters, where permission is checked, what gets stored and which external services are called. For incoming data, verify actual runtime checks against the expected contract; a static type assertion does not itself validate an untrusted value.

Review dependency changes deliberately

Inspect new or changed packages, their purpose, version and relevant license information. GitHub dependency review can expose dependency changes and known vulnerabilities in supported repositories. Check feature availability for the repository in question; do not assume a clean report establishes that every dependency is safe or appropriate.

Separate a known vulnerability from a maintenance concern or a possible license issue. An old release date is a reason to investigate suitability, not proof of a vulnerability. Refer unresolved licensing or security questions to the people responsible for those decisions rather than asking the author to guess.

Test the behavior the change promises

Run unfamiliar code in an appropriately isolated environment with controlled resources and without production credentials. Select tests from the acceptance statement and the system's interfaces. Depending on the change, relevant cases might include malformed input, denied access, an unavailable dependency, a duplicate request or two updates to the same record.

Expected behavior must come from the contract. An empty array, for example, may be a valid result rather than an error. Where the change starts background work or opens resources, examine cancellation, timeouts and cleanup. A passing formatter or linter cannot replace tests of the application behavior it does not evaluate.

Keep the review useful for learning and release

Have the author summarize what they changed after feedback and explain one tradeoff in their own words. Pair on a difficult test or debugging step instead of reducing the exercise to accepting a suggested patch. Preserve important reasoning in the pull request so another engineer can revisit it.

NIST's Secure Software Development Framework version 1.1 describes high-level security practices that can be integrated into a development lifecycle. Use that perspective to place review alongside other relevant controls, rather than treating AI-assisted code as a separate exemption or assuming one checklist proves security.

Before release, identify the responsible reviewer, evidence from testing, known limitations and the recovery approach. A staging result has the scope of the environment and cases actually exercised. Continue monitoring the released change and use any findings to improve both the software and the team's next review.