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Guide

How to Review AI Generated Code

By The Learn Cursor Editorial TeamUpdated

Review AI generated code by reading the diff first, checking scope, running tests, inspecting edge cases and looking for invented patterns. Do not review only the answer the agent gives you. The diff, commands and failures are the source of truth.

Cover image for How to Review AI Generated Code

What is the working pattern for AI generated code review?

Move
Start with a bounded task
Use this when
Engineers and reviewers who approve AI-assisted pull requests.
Proof to save
Issue, files, checks and owner are named
Move
Give the agent context
Use this when
The repo has patterns the agent must follow
Proof to save
Prompt cites files, errors and constraints
Move
Review the diff
Use this when
The task changes production code
Proof to save
Changed files, test output and risks are visible

A good AI coding workflow is specific enough to review and small enough to recover.

Inspectable agent workflow

Interactive diagram. Use Tab to move through hotspots or use the step controls when shown.

ContextFiles and taskPlanBefore editsChangeSmall patchTestEvidence▲ GATEReviewHuman handoff
1/5
Context: Name the issue, repo area, source files and constraints.

Use this loop when the change is larger than autocomplete and smaller than a full project rewrite.

Diff review sandbox

Interactive diagram. Use Tab to move through hotspots or use the step controls when shown.

Review call

src/api/billing.ts

Touches billing behavior. Require a regression test and reviewer signoff.

  • Read every changed file.
  • Check whether the prompt allowed this scope.
  • Run the narrowest command that proves the change.

Select a file and decide what proof the reviewer needs before merge.

Practice view
Learn Cursor module with a learning section, diagram and checks

The public guide connects to lessons, recall and readiness checks inside Learn Cursor.

How should a team run AI generated code review?

  1. 1Pick one real backlog item with a clear owner and expected result.
  2. 2Add only the context the agent needs: files, failing output, constraints and done state.
  3. 3Ask for a plan before code when the task touches more than one file.
  4. 4Run checks that match the risk: unit test, typecheck, visual pass or review checklist.
  5. 5Capture the prompt, diff, result and reviewer note so the workflow can be repeated.
Prompt frame

Task, context, constraints, done state and checks.

Review habit

Open the diff, read changed files and rerun the check yourself.

Team artifact

The page gives a review checklist and connects it to tests and changed files.

What should you keep after the run?

  • The prompt or plan that shaped the work.
  • The files changed and the reason each file changed.
  • The command, screenshot or review note that proved the result.
  • The rule, checklist or template you would reuse next time.

Frequently asked questions

Who is How to Review AI Generated Code for?

Engineers and reviewers who approve AI-assisted pull requests.

What makes this page credible?

The page gives a review checklist and connects it to tests and changed files.

What should I do next?

Start with one real repo task, capture the prompt and review the result before scaling the workflow.

Sources & last verified

Cursor ships frequently. Facts verified against primary sources on June 23, 2026.

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