Code X-Ray: AI Code Review for Cursor, Copilot & ChatGPT-Generated Code

Code X-Ray is a diff-first AI code review workflow designed for modern teams shipping fast with Cursor, Copilot, ChatGPT, and other LLM-powered coding tools. This guide explains how to review AI-generated code, secure AI-generated pull requests, verify LLM output, and catch AI coding security risks before they reach production.

Whether you need a cursor code review checklist, want to review ChatGPT code before merging, or need to secure AI-generated code in your CI/CD pipeline — this pillar guide and its cluster articles cover every angle.

Why AI Code Review Matters

AI-generated code from tools like Cursor and GitHub Copilot looks fluent and often passes basic tests. But it frequently introduces hidden risks: hallucinated dependencies, missing authentication, over-permissioned IAM roles, and silent cloud cost amplification.

Traditional code review assumes a human understood the system context. AI pull request review requires a different approach — one that verifies assumptions, checks for fabricated imports, and validates security enforcement.

Code X-Ray focuses on the diff to surface these hidden risks, then converts them into concrete fix plans and PRs.

The Core Workflow

Compare branches or SHAs to isolate the delta.
Generate a risk report that flags security, performance, reliability, and governance gaps.
Produce a fix plan (WHAT, WHERE, HOW, RISK, PATCH TYPE).
Create a Fix PR that ships a patch into a new branch.
Run evals (lint + tests) to prove the patch is safe.
Review and merge with clear evidence and context.

What You Get (Even If a PR Fails)

A plain-English risk report tied to the diff.
A fix plan with concrete patch strategy.
An eval plan with commands and expected outcomes.
Stored fix artifacts for support and recovery.

Best Practices for Teams

Make diff-first review part of your PR checklist.
Require eval runs for all Fix PRs before merge.
Use structured fix plans to reduce debate and speed review.
Log every Fix PR and eval run for audit and compliance.

AI Code Review Guides

These in-depth guides cover every aspect of reviewing AI-generated code — from Cursor code review to Copilot code review, ChatGPT output verification, and AI pull request security.

How to Review Cursor-Generated Code Before Merging (2026 Guide) — A step-by-step checklist for reviewing Cursor-generated PRs, covering hallucination detection, auth checks, cloud cost risks, and edge-case validation.
AI-Generated Code Security Risks You’re Probably Missing — The 7 most common security vulnerabilities AI coding tools introduce, and how to systematically detect them in pull requests.
Why AI-Generated Code Passes Tests But Fails in Production — Why LLM-generated code breaks under real-world conditions despite passing all tests, and how to prevent silent AI-induced failures.
Checklist for Reviewing LLM-Generated PRs — A 10-category technical checklist for reviewing AI-generated pull requests: intent alignment, auth enforcement, hallucinated dependencies, cloud cost, regression risk, and more.
AI Hallucinated This Dependency — Here's How to Catch It — Why LLMs invent fake libraries, nonexistent helper functions, and incorrect API surfaces — and how to detect them before merge.
Why AI-Generated Code Is Quietly Increasing Your Cloud Run Bill — How Cursor and Copilot-generated code silently increases Cloud Run and AWS costs through unbounded queries, N+1 patterns, and over-provisioned resources.

Who Needs AI Code Review?

If your team uses Cursor, GitHub Copilot, ChatGPT, Claude, or any LLM-powered coding tool, you need a structured AI code review process. Traditional review catches logical errors but misses AI-specific failure modes.

Solo developers and freelancers using Cursor or Copilot who lack a second pair of eyes.
Engineering teams reviewing AI-generated pull requests at scale.
Security-conscious organizations that need to verify AI-generated code before it reaches production.
Cloud-first teams whose infrastructure costs spike from unreviewed AI-generated patterns.