How to review AI-generated code changes in VS Code

How do I review AI-generated code changes in Visual Studio Code

Open Source Control and review every changed file in the diff editor before you commit anything. If Copilot or another AI agent made the edits, use its review view as a helper, not as the final check. Visual Studio Code documents Source Control, diff review, and AI change review as the normal path for keeping control of generated code.

The workflow is simple. Make or accept the AI changes, open the Source Control view, then click each modified file to open a diff. VS Code shows what changed against the last saved or committed version, side by side when there is room and inline when the window is narrow. That diff is the part you actually inspect, because it shows the exact lines that would enter your branch.

If you are using GitHub Copilot in VS Code, the Source Control view includes a Code Review action for uncommitted changes. VS Code also documents a code review flow inside the Chat and Agents experience, where you review changed files, request revisions, and use checkpoints to roll back earlier steps. That is useful, but it is still a review aid, not a replacement for reading the diff yourself.

Start with the file list, not with the model explanation. The file list tells you scope: which files changed, how many files changed, and whether the AI touched code, tests, config, or docs. People usually get this wrong by reading the AI summary first and only opening the obvious file. The missed file is often where the bug sits, especially in a helper, schema, or test fixture.

Then read the diff as if you were reviewing someone else’s pull request. Check whether the AI preserved intent, whether any branch condition changed, and whether a small-looking edit changes runtime behavior. Look at surrounding lines, not only the green and red blocks. VS Code’s diff editor is designed for that exact job, and its source control docs explicitly say to verify what the commit will contain before you stage it.

The inconvenient part is that AI edits often look clean while still being wrong. A generated refactor can move logic into a helper and quietly change null handling, error propagation, or data shape. A generated fix can satisfy one example and still break an edge case the prompt never mentioned. VS Code’s guidance on AI trust and best practices says to review for bugs, security issues, hardcoded secrets, missing validation, and assumptions before integrating the result.

Run the tests after the diff looks sane. Do not stop at a visual review, because many AI mistakes only show up when code executes. If the change touches business logic, run the narrow test that covers the edited path first, then run the broader suite if the blast radius is larger. VS Code’s AI guidance says to include tests in the prompt when useful, but to run them yourself before moving on.

Use checkpoints when the AI has drifted into a bad branch of work. VS Code documents checkpoints for restoring an earlier snapshot and undoing a request plus later file changes. That matters when a generated edit was technically valid but moved the codebase in the wrong direction, because a rollback is faster and safer than trying to salvage a cascade of partial edits.

Review the file types with different standards. For application code, verify logic, tests, and imports. For configuration files, verify that a changed setting affects the right environment. For tests, make sure the AI did not just rewrite assertions to match broken behavior. For generated docs, confirm the text reflects the code after the change, not the AI’s explanation of the change. The same diff editor works for all of them, but the questions you ask should change.

A practical way to work is to review in three passes. First pass, scan the file list and look for unexpected surface area. Second pass, read each diff and ask whether the code still does the same job. Third pass, run the tests and check the behavior at runtime. That sequence catches most of the failures people miss when they only glance at the final output from the agent panel.

If you are reviewing changes in a repository with Copilot instructions, read those instructions before judging the edit. GitHub documents repository-level Copilot instructions for code review, and they can change what the reviewer focuses on. A review that ignores project-specific rules wastes time, because the AI may have followed the prompt while still violating local standards.

If you want a short internal workflow, use this: open Source Control, open each diff, verify the behavior change, run tests, then stage only the files you trust. If the change came from chat or agent mode, keep the changes panel open and use revision prompts or checkpoints when the output drifts. That is the part that saves time without pretending the AI was right on the first pass.

If you need a place to coordinate testing work with other builders, DevConnect is free to use and focuses on reciprocal testing rather than paid shortcuts, so you can keep the review loop inside work you control. The page at https://devconnectplatform.com describes the platform itself. That is useful for organizing human review, but it does not replace checking the diff in VS Code.

A good final check is to ask what would break if the AI was subtly wrong. If the answer is, “nothing obvious,” that usually means the review was too shallow. Good review is not about trusting the model less in theory, it is about forcing every important change through the same concrete checks: diff, scope, tests, rollback path, and commit boundary.