GitHub's New Copilot Review Settings: When Should AI Check Your App Code?

An AI coding tool can finish a feature in an hour, but the feature may change several times before you publish it. Which version should someone review?

I ask because a review of the first draft does not cover a later edit. A review after every tiny push can become noise. And a green-looking AI comment does not tell you whether a real person can complete the intended workflow.

GitHub's September 23 update to Copilot code review makes that timing more explicit. Personal settings now offer separate automatic review controls for draft pull requests and new pushes, plus a default effort choice between Lite and Balanced. GitHub also added an enterprise default effort setting that organizations and repositories can override.

The lesson I take from this is broader than one paid review tool: choose review checkpoints by the decision you need to make, not by the number of times AI generated code.

For a first app, I would use three checkpoints: an early design check, a change check after risky edits, and a release check against the user's actual task. You can run this routine with a teammate, your own manual review, or a code review tool. If defining that first task is the hard part, my $1 AI App Builder Starter Prompts can guide the initial scope; give the reviewer a concrete job to inspect.

What changed, and what it does not mean

GitHub says the new personal page is available across Copilot plans, including Business and Enterprise. A user can choose automatic review for a pull request they create or coauthor, include draft pull requests, and request another review when new commits are pushed. A manual request can still use a different effort level from the default.

That is useful control, but turning on every trigger is not automatically a stronger safety net. GitHub's review documentation says a previously reviewed pull request is not automatically reviewed again after a push unless new-push review is configured. It also says the default Copilot response is a comment review, not a required approval. An approval assessment alone does not satisfy merge requirements.

There is a cost tradeoff too. GitHub describes Lite as targeted feedback and Balanced as deeper analysis, with more credit use for Balanced. I would decide which changes deserve that depth before turning on broad automatic review. You do not need a paid tool to apply the checkpoint plan below.

Checkpoint 1: Review the shape while the pull request is still a draft

Imagine you are building a simple appointment request app. A visitor submits a time preference, and the owner sees a pending request. The first draft works on the happy path.

Before polishing the interface, I would ask whether the change has the right shape:

• Where does a request enter the system?
• Which data fields are actually needed?
• What status means “pending” versus “confirmed”?
• Who can see or change that status?

The proof is a short description and one working end-to-end example, not a verdict that the app is ready to launch. Catching a missing ownership rule here can prevent you from polishing the wrong data model.

If a review tool is available, a draft pull request is a reasonable place for this question. If not, write the answers in the pull request description and walk through them yourself or with a collaborator. The checkpoint still exists.

Checkpoint 2: Review what a consequential push changed

Suppose a later commit adds sign-in so owners can see only their own appointments. The first draft review is stale. I would review the change before treating earlier feedback as still valid.

This time the question is narrower: “Can one owner see or edit another owner's request?” I would test with two separate ordinary test accounts and attempt the forbidden read and edit. Then I would inspect the server-side permission rule, not just the screen's visibility logic.

This is the moment when a new-push review or a manually requested re-review has a purpose. It looks at code after a consequential change. It does not replace the two-account test, because a reviewer can miss behavior that only appears with real roles and data.

I would not request a deep review every time I rename a label. I would request one when the change crosses a boundary: identity, payments, data ownership, deployment, or a shared component used across important flows. That is my risk rule, not a GitHub default.

Checkpoint 3: Review the user journey before release

At the end, I would stop reading the pull request as a pile of files and try the task as a visitor and an owner:

1. Submit a request with valid information.
2. Submit one with a missing or invalid field.
3. Refresh and confirm the request persists.
4. Sign in as the owner and change its status.
5. Sign in as a different owner and verify that the first owner's data stays inaccessible.

Now a final reviewer has a useful question: “Does the current version meet this journey, and which risk remains?” A review comment can point to a likely defect. A passing manual journey can show the actual behavior. Neither proof is complete by itself.

I would record the result beside the pull request: tested version, accounts or roles used, result, remaining gap, and the person who decides whether the gap is acceptable. If another code change lands after that check, I would revisit the affected step.

Give the reviewer a job it can understand

Generic “review my app” instructions tend to invite generic feedback. I would write one or two project-specific checks, such as “owner-only data reads must be enforced on the server” or “a request must survive refresh before it is described as submitted.”

GitHub's guide to review instructions recommends concise, specific guidance and warns that AI review does not follow every instruction perfectly. It also says Copilot reads instruction files from the pull request's head branch. That means a changed instruction in the same branch can affect the review. I would inspect those files as part of the change, especially when the review is supposed to check a sensitive rule.

This is a limitation worth keeping in plain sight: an AI reviewer offers findings, not independent proof that everything important was examined. I still need tests, direct use of the app, and human judgment about what ships.

The next build session

Before your next AI-assisted feature, write three lines:

Draft: What design assumption should be challenged early?

Change: Which later edit would make that review stale?

Release: What user journey must work in the current version?

That gives a review its timing and its job. It also gives you a way to notice when the evidence has expired. A faster coding tool does not remove the need to know which version was actually checked.

If you want a guided first step, use the $1 AI App Builder Starter Prompts to define a small feature, then add these three review questions to your build note.

For an organized path from idea through scope, architecture, QA, and publication, AI App Builder From Zero is my $9 e-book. All 40 Starter Prompts are included as a free bonus inside its PDF and EPUB, so you do not need to buy the standalone pack as well.

Review Radar is coming soon: preview it and join the email waitlist. It is being built to turn research about your idea into tailored app screens and an AI-ready project folder. Those materials can make a build more concrete; they are not a promise of revenue, a finished app, or a substitute for the review and journey checks above. Checkout is not open.

You can also find me here:

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