A Practical Guide to AI Coding Assistants and Code Review

Ask an AI assistant to add a discount field to a checkout function and it may produce a plausible patch in seconds. The important review question is whether that patch preserves currency rounding, authorization, and existing callers. A coding assistant can help draft an implementation or find suspicious changes, but the team still needs a way to establish what the code does. This guide builds that review around a concrete change rather than the assistant's confidence.

Write the contract before requesting the patch

Describe inputs, outputs, invariants, and exclusions. For a discount calculation, specify the currency representation, allowed range, and behavior when the discount exceeds the subtotal. Name the existing function and relevant tests. State whether the assistant may change dependencies or public interfaces. A short task with clear boundaries is easier to inspect than a request to improve the entire checkout experience.

Give the assistant the smallest context that answers the task accurately: the target module, its callers, relevant data types, and project conventions. Avoid uploading credentials, customer exports, or unrelated private material. If you cannot describe why a file is needed, leave it out initially. Missing context can be added deliberately; unnecessary sensitive context cannot always be retrieved after sharing.

Ask for an explanation of assumptions before accepting a broad edit. Distinguish an assumption such as prices are integer minor units from a verified fact found in the repository. If the repository contradicts the assumption, correct the implementation rather than adding a comment that preserves the mistake. Keep a human-readable requirement beside the patch so review does not depend on reconstructing the original conversation.

Use AI review as an additional reading pass

GitHub describes Copilot code review as a way to inspect changes and suggest fixes. That capability can help identify questions worth investigating. It does not mean a suggestion is correct for your application or that an unmentioned defect is absent. Ask an assistant to identify concrete failure paths, and require a file location, trigger, and consequence for each proposed issue.

For every finding, reproduce the reasoning independently. Trace the relevant values through the code, inspect the actual library API, and check whether the path is reachable. Reject speculative warnings that cannot be tied to the change. Equally, do not dismiss a finding because it came from a model. The useful unit is a verifiable claim about behavior, regardless of who proposed it.

1. Specify the contract and collect the minimum relevant context.
2. Generate a focused patch on an isolated branch.
3. Read the complete diff and trace changed behavior through callers.
4. Check model findings against documentation and meaningful tests.
5. Record remaining limits and obtain the required human review.

Read the diff for changes in authority

Begin with interfaces, authentication, persistence, and external effects. A patch that adds a convenient parameter may also allow a caller to choose an account identifier that was previously derived from the session. A retry loop may duplicate a charge. A logging statement may expose a request body. These are more consequential than whether the generated variable names match your favorite style.

Then inspect failure behavior. What happens when an upstream service times out, returns malformed data, or succeeds before the connection breaks? Does the patch catch errors so broadly that a failure becomes a successful empty result? Has it changed a synchronous operation into background work without changing the caller's expectations? Review those transitions explicitly instead of relying on a successful build.

Tests should challenge the requirement, not merely repeat the implementation. For the discount example, include no discount, a boundary value, an invalid value, and an existing caller that omits the new option. A test using the same incorrect formula as the generated code can pass while the behavior remains wrong. Keep expected results independently understandable, and do not claim tests ran when only a test file was written.

A hypothetical checkout change

Consider a hypothetical shop whose subtotal is 2500 minor currency units. The proposed feature allows an authorized promotion to subtract 300 units, leaving 2200. The requirement also says that ordinary clients cannot submit arbitrary promotion amounts. A generated patch that accepts a browser-provided discount may calculate correctly yet violate the authorization contract. The review needs to identify both concerns separately.

The reviewer checks where promotions are loaded, whether the server validates eligibility, and how the final amount is stored. They create synthetic cases for expired promotions and a discount larger than the subtotal. These are example review steps, not reported test results. If the change needs a schema migration, they request a separate explanation of deployment order and rollback rather than treating that migration as incidental code.

GitHub's pull-request review documentation distinguishes comments, approvals, and requests for changes, and describes required review controls. Use that distinction honestly: a model-generated comment is not the same as the accountable review required by your repository policy. The final review record should state what was verified and which checks remain pending.

Checklist before accepting assistant-written code

• Compare the patch with a written behavior contract.
• Read every changed file, including lockfiles and generated configuration.
• Trace authorization, data validation, and external side effects.
• Verify unfamiliar APIs against their current primary documentation.
• Run tests that distinguish correct behavior from plausible alternatives.
• Investigate each concrete AI finding rather than accepting it automatically.
• Keep review ownership and deployment authority explicit.

AI assistance is most useful when it reduces drafting effort while leaving the evidence easy to inspect. A tiny formatting patch does not need the same ceremony as a billing change. Scale the review to the consequence, keep the scope narrow, and preserve enough context that another maintainer can understand the decision after the chat is gone.