The idea

An AI coding assistant can produce a patch or a pull request, a proposed set of changes for review. The output may look convincing because it is formatted well and resembles code you expect to see. That is not evidence that it solves the right problem, works at the edges, or fits your project.

Reviewing AI code means treating it as a proposed contribution. First, decide what behavior the task requires. Then inspect the change, run relevant checks, and make the merge decision yourself. GitHub recommends understanding generated code and checking its functionality, security, readability, and maintainability before use. It also recommends comparing a change with the project’s purpose, requirements, and architecture: the project’s overall structure and how its parts work together. GitHub guidance GitHub best practices

Plain English: AI can write a first draft of a change. You are still responsible for deciding whether that change belongs in the product.

A useful review loop has four parts:

  1. Restate the expected behavior in your own words.
  2. Read the diff, the line-by-line view of what changed. Ask why every changed file and dependency, an external library or component the project relies on, is needed. Check the security implications too.
  3. Run automated tests, checks that execute code and compare results with expected behavior. Add a focused test if an important case is missing.
  4. Run static analysis: tools that inspect code without executing it. This can include linting, which checks style and likely programming mistakes, and code scanning, which looks for potential security or code-quality issues.

GitHub specifically recommends automated tests and static tools such as linting and code scanning when validating AI-generated code. These checks complement reading the change; they do not replace it. GitHub guidance

A small example

Example: You ask an assistant to normalize email addresses in a Python API. Its proposed change trims surrounding whitespace and lowercases the address.

Start with the requirement: “Accept an address with surrounding whitespace, return the normalized value, and reject invalid input in the same way as before.” Read the diff. Did it change only the validation path? Did it alter error handling? Did it add a library for work that the project already handles? What security consequences could the change have?

Next, examine the logic and run existing tests. Add small tests for uppercase input, surrounding whitespace, invalid input, and an already-normalized address. Then run the project’s linting and other static checks. A passing result gives useful evidence, but it does not prove the behavior is correct in every situation.

Also review tests written by the assistant. A test can pass while checking only the implementation the assistant chose, rather than the behavior users need. GitHub notes that generated tests may miss relevant scenarios or error cases. GitHub guidance

Try it

Set aside five minutes for one small AI-generated change, or use a hypothetical change if you do not have one.

Write one sentence describing the required behavior. Open the diff and name each modified file’s purpose. Pick one boundary or failure case that is not clearly tested. Finally, run the checks already available in the repository and record one question that remains. If the change is not understandable, it is not ready to merge.

Watch out

Tests only cover the cases they express. Static analysis also has limits: it can miss problems and can report findings that need human interpretation. Security, privacy, licensing, and architecture decisions may require extra expertise or review.

Do not treat an AI reviewer as a final authority either. GitHub says that pull requests produced by Copilot should receive thorough human review before merging, like other contributions. GitHub guidance

The goal is not to prove that code is perfect. The goal is to make a deliberate, evidence-based decision before it reaches users.