The idea

An AI coding assistant can write a useful patch quickly. That does not make the patch correct for your application. A suggestion can look convincing while using an API that does not exist, missing a constraint, or implementing the wrong logic. GitHub specifically identifies these as risks when reviewing AI-generated code. GitHub Docs

The habit to build is simple: treat AI output as a proposed code change, not as proof. Before you keep it, inspect it with tools and with your own understanding.

Plain English: AI can draft code. You remain responsible for deciding whether that code should enter your project.

Start with the diff, the view that shows exactly which files and lines changed. It answers a basic question: did the assistant change only what you expected? A request to handle one input error should not quietly rewrite unrelated configuration or public interfaces.

Next, run relevant automated tests. These are repeatable checks that execute code and compare actual behavior with expected behavior. Also run static analysis, which examines code without running it. This can include a linter, type checker, or code scanner. GitHub recommends automated tests and static analysis as early checks for AI-generated code. GitHub Docs

Those checks are useful, but they do not answer every question. Read the change and compare it with the requested intent: the problem the change is supposed to solve. Then consider whether it fits the project’s architecture, meaning the system’s chosen structure and boundaries. GitHub recommends assessing whether generated code matches the project’s purpose, requirements, and architecture. GitHub Docs

Finally, assess functionality, security, readability, and maintainability. You should understand proposed code before accepting it. GitHub Docs

A small example

Example, hypothetical: You ask a coding assistant to update a function that reads CSV files. The goal is to skip blank rows while preserving useful error messages for malformed rows.

First, open the diff. You expect a small change in the CSV reader and perhaps its tests. If the suggestion also adds a new dependency or alters an unrelated parser, stop and find out why.

Second, run the existing tests and your project’s static checks. A passing result says the checked cases still work. It does not prove that the new requirement is complete.

Third, add or inspect a test for an edge case: an unusual but meaningful input near a boundary of normal behavior. Here, a blank line between two valid rows is an edge case. You might also inspect a file containing only blank lines and a malformed nonblank row.

Fourth, read the error-handling branch. Does a blank row get skipped without also hiding a malformed row? Does the change match the surrounding style and the function’s callers? This is where you check intent and project context, not merely syntax.

Do not trust a generated test just because it passes. GitHub notes that AI-generated tests may fail to cover every scenario, so they need review too. GitHub Docs

Try it

Set aside about five minutes for this hypothetical exercise:

  1. Pick one small AI-generated suggestion or an old small patch.
  2. Read its diff. State what changed in one sentence.
  3. Run one relevant test or static check.
  4. Name one edge case the current tests do not clearly cover.
  5. Read the changed code and explain, in your own words, how it handles that case.

If you cannot explain the change, do not merge it yet. Ask for an explanation if useful, but verify that explanation against the code and checks. An AI explanation is also AI output, not independent evidence.

Watch out

A green test run is evidence, not a guarantee. Tests only cover the cases they contain. Static analysis catches many repeatable issues, but it cannot reliably decide whether business rules, architecture, or user-facing behavior are right.

Be particularly careful when a suggestion introduces a dependency, changes permissions or input validation, alters a public interface, or touches security-sensitive behavior. The available scanning and dependency tools vary by project, so use the checks your project actually supports.

The goal is not to distrust every suggestion or to perform an enormous review for every typo fix. Scale the review to the change. But keep the sequence: inspect the diff, run checks, read the code, and judge its fit. That routine lets you use AI assistance while keeping the engineering decision where it belongs: with you.