Resource
AI Code Review Checklist
Use this checklist to review AI-generated or AI-assisted code before you trust it to merge, deploy, or hand off to another coding agent.
The point is practical discipline: define the work, inspect the blast radius, verify the build, and decide whether the change is ready, risky, or better split into a smaller slice.
Purpose
A checklist for AI-assisted code before trust is extended
This resource helps teams review AI-generated or AI-assisted changes before they become a merge, a deployment, or someone else’s next starting point.
Use it to decide whether the work is ready, needs more review, should be split apart, or should be repaired before it moves any further.
Checklist
Review areas that matter before merge or deployment
Each section keeps the review concrete and bounded to the actual implementation risk.
Task scope and acceptance criteria
- What changed, and what problem was the change meant to solve?
- Are the acceptance criteria explicit enough to verify without guessing?
- Does the diff still match the original task, or did it drift?
Diff size and blast radius
- Is the change small enough to reason about safely?
- Which routes, components, jobs, or data paths can this touch?
- Does the diff introduce unrelated refactors or hidden coupling?
Build and test status
- Has the current branch been built successfully?
- Which tests directly cover the changed path, and which ones are missing?
- Are failures known, new, or unrelated to this work?
Architectural fit
- Does the implementation fit the existing route, data, and UI structure?
- Is the change aligned with the established abstraction level?
- Does the code preserve the repo’s intent instead of adding unnecessary complexity?
Auth and permissions
- Who can reach this flow, and should that remain unchanged?
- Are permission checks explicit where they need to be?
- Could AI-generated edits weaken access boundaries or assumptions?
Secrets and config boundaries
- Does the diff touch environment variables, credentials, or provider settings?
- Are any secrets, service-account files, or private keys being exposed or assumed?
- Can the change run without introducing new hidden configuration risk?
Data model and migrations
- Does the change require schema updates, migrations, or backfills?
- Are data writes and reads still compatible with existing records?
- Is rollback possible if the data shape turns out to be wrong?
Error handling and logging
- Are failure states explicit instead of silent?
- Will logs help diagnose the problem without leaking sensitive data?
- Does the code fail safely when an upstream dependency is unavailable?
Dependency changes
- Did the change add or upgrade packages, and is that necessary?
- Are transitive risks or version conflicts worth reviewing before merge?
- Does the diff depend on a new vendor or SDK without a clear need?
Test coverage gaps
- What behavior is covered now, and what still needs a test?
- Did the AI-generated change create a blind spot in route, API, or UI coverage?
- Would a focused regression test reduce the risk materially?
Human review ownership
- Who owns the final judgment on this code?
- Is the right human accountable for the merge and follow-up?
- Should another AI agent continue, or should the work stop for review?
Rollback and deployment readiness
- Can the change be reverted quickly if it misbehaves?
- Are release notes, rollout steps, or monitoring expectations clear?
- Would you trust this code in production today?
Use it when
A simple way to apply the checklist
The checklist is most useful at the points where AI-generated code can quietly outrun judgment.
Before merging AI-generated changes into the main branch.
Before asking another AI or coding agent to continue the same work.
Before deployment when the code is close but not fully trusted.
Before deciding whether to repair, revert, or split the task into smaller pieces.
Connect this checklist to the consulting path
If the review reveals delivery risk, unclear ownership, or an app that needs a cleaner technical decision, move toward the relevant WinMedia service path.
Boundaries
What this checklist is not
The checklist is meant to support judgment, not replace it or overstate what AI code review can prove.
- not a security certification
- not a guarantee of production readiness
- not a substitute for human review
- not legal, medical, financial, or regulated professional advice