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IntermediateEngineering

Implement Model-vs-Model AI Code Reviews for Quality Control

Use one model to build quickly and a second model to review the diff for plan mismatches, code smells, and better architecture before applying the fixes.

Implement Model-vs-Model AI Code Reviews for Quality Control

CJ sends Claude’s implementation and planning artifacts to Codex, which catches a visual mismatch, a hook issue, and refactoring opportunities before applying the fixes.

Before you start

What you need

  • A completed feature branch or current git diff
  • The plan, diagrams, mockups, or acceptance criteria used to build the feature
  • A second AI model that did not produce the original implementation

What you’ll make

A specific review report and a cleaner implementation that still matches the approved behavior.

Tools used

  • Claude

    Anthropic AI assistant

    VisitClaude
  • Codex

    OpenAI's cloud-based AI software engineering agent that can execute code, run tests, and handle complex multi-file tasks autonomously.

    VisitCodex

Step by step

The workflow

Follow the sequence once, then adapt the prompts, checks, and handoffs to your own setup.

4 steps

Step01

Generate Feature Code

Use your primary coding agent to implement the feature from the available plans or acceptance criteria. Get to a working diff before switching models.

Example prompt
Implement [feature] using the attached plan and mockups. Run the relevant checks, then summarize the files changed, any assumptions you made, and anything that still differs from the plan.

Keep the original planning artifacts in the repository so the reviewer can compare intent with implementation.

Step02

Initiate a Structured Code Review

Give the current diff and the original planning artifacts to a second model. Ask it to review plan alignment, code quality, and whether a cleaner design would improve the codebase.

Example prompt
Review the current git diff and the attached plan or diagram artifacts. Report on: 1. Does the implementation accurately reflect the plan? 2. Are there correctness issues or code smells? 3. If we rebuilt or refactored this feature, what approach would improve the codebase? Cite the relevant files for each finding and rank findings by impact.

A separate model is useful here because it brings different defaults and is less invested in defending the first implementation.

Step03

Analyze the AI-Generated Feedback

Separate confirmed defects from optional cleanup. In CJ’s example, the useful findings included a spinner alignment mismatch, a missing hook dependency, and opportunities to extract components and constants.

Reject findings that cannot point to the diff, the plan, a failing check, or a concrete maintenance cost.

Step04

Implement the Suggested Improvements

Have the reviewing model apply the approved fixes, then rerun tests and compare the result with the original artifacts.

Example prompt
Apply the confirmed correctness fixes and the approved refactors from your report. Preserve the intended behavior. Run the relevant tests, inspect the final diff, and list anything you chose not to change.

What good looks like

  • The review covers plan alignment, correctness and code smells, and architectural improvements as separate questions.
  • Every important finding points to a concrete part of the diff and explains why it matters.
  • After the fixes, tests and a visual check confirm the behavior still matches the original plan.

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After the steps

Runbook notes

How to recover when the loop fails and where human judgment helps.

Recover

If it goes sideways

The second model returns generic review advice
Provide the current diff and planning artifacts, require file-level evidence for every finding, and ask it to rank issues by impact.
The reviewer proposes a large rewrite without proving it is necessary
Split the report into required correctness fixes and optional refactors. Apply the correctness fixes first, then take only refactors with a clear maintenance benefit.

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