How to Review AI Coding Changes: Why “Done” Is Not Enough

1. The Cost of Being Misled by “Done”

In the previous stage, you asked an AI coding tool to generate your first webpage. When the page actually appeared in the browser, the feeling was magical: you only said a few things, the code was written, and the page opened. As a result, many people see the tool reply “Done,” feel satisfied, and close the window.

But remember a key distinction: “Done” only means this round of conversation or task execution has ended. It does not mean the result necessarily meets your actual requirements.

It may have completed the task correctly. It may also have done one or more of the following:

• Changed files that should not have been touched.
• Deleted an existing feature.
• Silently installed new dependencies you were not aware of.
• Written “suggest running tests” in the reply without actually running any tests.
• Looked fine on desktop but produced a broken layout on mobile.

From this stage onward, what we need to learn is not only “how to make AI write code,” but something more fundamental and more important: reviewing AI work results. If you cannot review the work, you cannot tell whether what you receive is a finished product or a hidden problem.

2. The Three-Layer Structure of Review

Every time a tool reports completion, do not end the task immediately. At minimum, go through the following three checks, forming a funnel from broad to detailed:

1. Which files did it change? (scope layer)
2. What specifically changed in each file? (content layer)
3. Can the project still run normally after the changes? (result layer)

These three layers are all necessary. Looking only at the first layer, you do not know what was modified. Looking only at the second layer, you do not know whether the whole project still works. Looking only at the third layer, it is difficult to locate the source if something goes wrong. Only when the three layers are connected can they form an effective review.

3. First Step: Check Which Files Were Changed

In different interfaces, you may see names such as “Review,” “Diff,” “Changes,” or “View changes.” Treat them as one idea: compare the content before and after the modification.

In desktop versions of mainstream AI coding tools, this area is often called the “Review pane.” If the project is already under version control, you can view changes, add line-by-line comments, and decide what to keep, stage, or revert. The usual color convention is:

• Green represents added content.
• Red represents deleted content.
• The file list represents the files changed within the current scope.

There is an easily overlooked detail: the Review pane shows changes detected by version control, not necessarily all changes made by AI. Content you modified but have not committed will also appear in the list. So the first thing to confirm is: which changes are mine, and which changes are from the tool?

If you are a complete beginner and cannot understand the code, that is fine. The first review does not require studying logic line by line. You only need to confirm one thing: did it touch things it should not have touched?

4. Second Step: Identify Warning Signs

After obtaining the list of changes, pay special attention to the following warning signs. They are often the earliest symptoms of problems:

• Unexpectedly large change scope: You asked it to change the color of one button, but it refactored five files. The larger the scope, the higher the probability of introducing unknown problems.
• Dependencies you did not request: New packages suddenly appear in the dependency manifest file. Adding dependencies means introducing external code, which may bring security risks, increased size, or version conflicts.
• Existing features deleted or commented out: In the red deleted lines, there is business logic you never intended to remove.
• Configuration files or secret files touched: Files involving environment variables, credentials, or permissions require special caution once they change.
• “Suggest running tests” but no test results visible: Real tests should have clear pass/fail output, not just a verbal suggestion.

A practical principle is: the more unexpected a change is, the slower your review pace should be. Any change beyond your original intent should be confirmed separately, rather than being allowed to slip through among many normal changes.

5. Third Step: Confirm the Project Can Actually Run

After understanding “what was changed,” you must finally answer “can it still be used?” This step is the easiest to skip, but also the most indispensable.

At minimum, verify three things:

1. Are dependencies complete and installable?        — Can the project start without installing new packages?
2. Can the main flow be accessed normally?          — Open core pages and complete key actions.
3. Is performance consistent across platforms?       — Check both desktop and mobile.

The judgment criterion is simple: the modified project must still pass the checks that passed before the modification. If the page opened before the change but does not open after it, or if it looks fine on desktop but breaks on mobile, then the change is not acceptable, regardless of whether the tool says “Done.”

If the project has automated tests or a build process, make sure to actually run them and read the output, rather than trusting the tool’s claim that “tests have passed.” Only when you see the green passing result yourself does it count.

6. Turn Review into a Habit

If you turn the above actions into a fixed process, you will find that efficiency in working with AI is actually higher:

• Deliver in small steps: Ask the tool to do one small thing at a time. The smaller the change, the easier it is to review.
• Ask about scope first: Before starting a task, clarify “which file to change only, and what not to touch.”
• Use version control as a safety net: Before making changes, ensure there is a baseline that can be rolled back, so you can restore it with one click if something goes wrong.
• Check layer by layer: First look at the file scope, then look at the specific diff, and finally run verification.
• Always check anything suspicious: Any change beyond expectations must be confirmed separately before deciding whether to keep it.

Reviewing is not a sign of distrusting AI; it is a sign of being responsible for your project. Tools can produce efficiently, but the responsibility for judging whether the output is good or bad always lies with the person. Only when you learn to inspect changes, understand diffs, and run verification can you truly master this collaboration method, rather than leaving your project’s fate to a casual “Done.”