Use case
How do you approach this?How to Review AI-Generated Code Safely
Published
Quick answer
Review AI-generated code as a proposal, not as proof. Restate the required behavior in your own words, inspect where the change crosses inputs, permissions, storage, or network boundaries, and run focused checks for the ordinary case and the riskiest edge. Polished naming and a confident explanation are not evidence that the code fits your application, dependencies, or team conventions.
Start with behavior you can verify
Read the ticket, failing test, API contract, or product rule before you read the generated explanation. Put the expected input, output, failure behavior, and boundaries into a short statement, then compare every important branch in the diff against that statement.
Spend review time at the edges
Generic code often looks most convincing in the middle of the happy path. Review validation, authorization, null handling, retries, persistence, and error reporting deliberately because those are the places where a plausible default can quietly become a production defect.
Checklist
- Can I state the intended behavior without repeating the generated explanation?
- What happens with missing data, rejected access, retries, or an external failure?
- Does the diff follow the surrounding codebase's error and test conventions?
Practice loop
- 01Write the required behavior and two failure cases before opening a generated diff.
- 02Mark every input, permission, storage, and network boundary in the proposed change.
- 03Run or add the smallest focused test for the highest-risk boundary before merging.
Limits of this page
This checklist improves evidence gathering, not certainty. A review still needs the local product context, dependency documentation, and a risk-appropriate test strategy; a short diagnostic session cannot establish why someone missed a review detail.
Source context
Editorially reviewed for Unrust using public language documentation, common code-review practice, and small original examples written for this resource system.
Check your own starting point
One diagnostic session is a snapshot, not causal proof of why a result occurred. It can help you choose a focused next practice step without making a broader claim about your ability.
Take the free diagnostic