An AI IDE can turn a short request into changes across components, configuration, tests, and documentation. The useful question is whether you can explain and verify the result. A reviewable Git workflow gives each task a clear purpose, keeps its changes identifiable, and records the evidence that the finished behavior meets that purpose.
This workflow applies to editor assistants, terminal agents, and coding tools connected to repositories. Their permissions and features vary. Start with the capabilities actually available in your environment, then use the AI development hub to connect task design with repository practices.
Define an outcome before asking for edits
A useful task describes observable behavior. Instead of asking an assistant to improve a search page, specify that an empty query should show a helpful message, that the message should be announced accessibly, and that existing filters should keep working. Include a concrete example of an empty result and an ordinary successful search.
List the files or components you expect to be involved when you know them. Treat that list as orientation, then require an explanation if the work expands beyond it. An assistant may find a shared component that needs changing, but the reason should be visible. Unexplained scope growth is a signal to inspect the plan.
Agree on what constitutes completion: the behavior implemented, the relevant check performed, and any documentation updated. A task can also have explicit boundaries, such as preserving the public interface or using the existing dependency set. These constraints make review easier because they give you something more precise than a general impression of quality.
Establish a known starting point
Inspect the repository before the assistant starts. Read git status, identify the current branch, and note any existing edits that belong to another task. Those edits need a deliberate place in the workflow. Do not let an automated cleanup erase work simply because it does not belong to the new request.
For a simple task with a clean starting state, a dedicated branch is often sufficient. In an existing repository, git switch -c improve-empty-search creates and switches to a new branch from the current commit. Confirm that the current commit is the base you intended before doing so.
When you need another working directory, Git worktrees provide separate checkouts associated with the repository. This can be useful for concurrent tasks or side-by-side verification. A worktree is an organization mechanism, however; do not assume it prevents a tool with broad filesystem access from reading other directories.
git status
git switch -c improve-empty-search
git statusKeep the setup proportional. A small copy change does not need an elaborate coordination system. A task that changes shared application state deserves clearer isolation and a more careful handoff. Choose the workflow according to the cost of mixing or misunderstanding the changes.
Give the assistant the context a reviewer will need
Point to the existing implementation, a nearby example that follows the project's conventions, and the command that exercises the affected behavior. If a project uses a particular error format or accessibility pattern, name it. Concrete references are easier to follow than a long list of general instructions about writing excellent code.
Keep real credentials and unrelated private material out of task context. Review the tool's configured file access, command permissions, and data handling for the environment you use. A private repository setting describes repository access; it does not, by itself, describe every connected AI tool's handling of code or prompts.
Ask for a short explanation when the assistant proposes a new package, a schema change, or a broad refactor. Those decisions affect future maintenance. A task can still justify them, but the reviewer should be able to connect the added complexity to a requirement that the existing code cannot satisfy cleanly.
Review the result in a deliberate order
Begin with the changed-file list. Compare it with the expected scope. New lockfiles, generated output, configuration changes, or deleted tests deserve attention because they can alter how the project builds and runs. Then read the complete diff, including deletions, before focusing on individual lines.
Follow one representative user action through the changed code. For the empty-search example, trace the input, query state, response, and rendered message. Check what happens when the user submits again or changes a filter. This approach reveals interactions that a neat-looking component may hide when examined alone.
GitHub's guide to reviewing AI-generated code emphasizes verifying functionality, project intent, dependencies, and code quality. Treat those as review dimensions. An AI summary can help identify areas to inspect, but the evidence remains the implementation and the behavior you verify.
Check the assistant's explanation against the actual files. If it says that an interface was preserved, inspect the signature and callers. If it says that no dependencies changed, read the package and lockfile diff. Summaries are useful navigation aids; verifying their specific claims prevents a confident explanation from substituting for review.
Choose checks that can expose a real failure
Start with the project's established checks relevant to the change. For behavior involving several components, include an integration check that exercises their connection. For a visual update, inspect the rendered page and operate the affected control. A successful build is useful evidence, but it cannot establish every interaction or layout requirement.
For a bug fix, define a case that would reveal the original bug. If practical, demonstrate that the old behavior fails that case and the new behavior passes it. Avoid a test that merely repeats the implementation's assumptions. A useful test should tell you when the promised outcome disappears during a future edit.
Record limitations precisely. If a service credential is unavailable, identify the integration that was not exercised and the local behavior that was checked. If a visual result was inspected at only one size, say so. A clear boundary around the evidence helps the next reviewer decide whether additional verification is necessary.
Make the commit and handoff readable
Stage the files or change sections that form the completed task. Read the staged diff again, especially if you edited the code after the assistant finished. If the staging distinction is unfamiliar, review working trees, staging, and commits before relying on a one-click commit action.
Write a message that names the behavior changed. In the handoff, explain the original problem, the resulting behavior, and the checks performed. Include a remaining limitation only when it affects confidence or deployment. Someone should understand why the change exists without reconstructing the full chat that produced it.
Keep the merge and deployment process consistent with the repository's established rules. If a change modifies stored data, explain the migration and recovery implications before release. If it changes only static presentation, the handoff can be much smaller. The right amount of detail follows the consequence of the change.
Common mistakes that make review harder
Repeatedly asking for broad improvements can accumulate unrelated edits. Break an unclear request into a specific outcome before generating more code. Another common problem is letting the same assistant continually rewrite its own tests until they pass without independently revisiting the requirement. Passing tests matter only when their assertions describe the desired behavior.
Watch for changes that suppress errors without resolving them: removed validation, empty exception handlers, weakened types, or skipped checks. Each may have a legitimate use in context, but it needs an explicit reason. Ask what failure the change now exposes to the caller and what evidence shows that the handling is appropriate.
Frequently asked questions
Should an AI assistant review its own changes?
A second pass can find omissions and explain difficult code. Use it as additional feedback, then inspect the important claims yourself or ask a teammate to review them. Shared assumptions can survive several automated passes, particularly when the requirement was ambiguous at the start.
Does every AI edit need new tests?
No. Choose verification that matches the behavior and risk. A documentation correction may need a careful read and link check. A change to authorization or data transformation needs stronger behavioral evidence. Avoid creating tests whose only purpose is to mirror a trivial implementation detail.
Can this workflow work with self-hosted Git?
Yes. The task, branch, review, and evidence practices apply regardless of where the remote lives. Tool compatibility and permissions still need checking. The Git hosting workflow comparison helps separate repository hosting choices from the way your team reviews changes.
Measure progress by explainable changes
A productive AI workflow ends with a change that another person can understand, verify, and maintain. Keep the task specific, the diff focused, and the evidence tied to user behavior. That makes AI assistance useful throughout the life of the repository, including the day someone needs to change the code again.
