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AdvancedEngineering

How to Combine Claude Opus and GPT-5.3 Codex for High-Velocity Code Refactoring

Pair Opus 4.6 as the eager builder with GPT-5.3 Codex as the rigorous reviewer: let one create the broad change, then let the other challenge its architecture, performance, and edge cases before shipping.

How to Combine Claude Opus and GPT-5.3 Codex for High-Velocity Code Refactoring

Source episode · 22:00

How I AI: GPT-5.3 Codex vs. Claude Opus 4.6—Shipping 44 PRs in 5 Days

with Claire Vo

Claire uses Opus to build and refactor quickly, then asks GPT-5.3 Codex for architecture, performance, and edge-case review before allowing it to implement the highest-impact fixes.

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Before you start

What you need

  • A scoped feature or refactor
  • Opus 4.6 in Cursor or Claude Code
  • GPT-5.3 Codex with repository access
  • The repository test and review commands

What you’ll make

A functional implementation that has been independently reviewed and hardened for production.

Tools used

Step by step

The workflow

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

5 steps

Step01

Build the First Draft with Claude Opus 4.6

Give Opus the feature or refactor goal, current pain, constraints, and relevant repository context. Use plan mode, resolve major design questions, then let it build a coherent first pass.

Example prompt
Refactor our tool components for GitHub, Linear, and other connectors into a reusable but customizable structure. Inspect current variants, propose a plan, preserve behavior, avoid overfitting to one connector, and implement a working first pass with tests.
Step02

Prepare Code for Principal-Level Review

Run the code and inspect the diff. Confirm the intended behavior works, tests cover the main path, and the change is focused enough for an architectural review.

Step03

Submit Code to GPT-5.3 Codex for Rigorous Review

Open the same repository or branch in Codex and request principal-level review. Ask for evidence-backed findings across architecture, performance, scalability, compatibility, and edge cases.

Example prompt
Review this implementation before shipping. Evaluate architecture, performance, scalability, customization boundaries, regressions, and edge cases. Do not rewrite it yet. Rank findings by severity, cite the relevant files and behavior, distinguish intentional tradeoffs from defects, and propose the smallest safe fix for each confirmed issue.
Step04

Analyze and Implement Codex's Feedback

Resolve Codex’s questions and reject findings that conflict with intentional product behavior. Approve the high-impact fixes and capture the decisions so the next model pass has the same context.

Step05

Finalize and Ship

Have Codex or Opus implement the approved fixes, run the repository checks, inspect the final diff, and send the branch through the normal PR review and preview process.

Example prompt
Implement only the approved findings: [list]. Preserve these intentional decisions: [list]. Run [tests, typecheck, lint, build], summarize the evidence, and leave the branch ready for independent PR review.

This workflow mimics a real-world engineering team dynamic. Use Opus for speed and building, and use Codex for rigor and quality assurance. This pairing leverages the unique strengths of each model.

What good looks like

  • Opus reaches a working first pass without over-scoping the task.
  • Codex ranks concrete issues with file and behavior evidence.
  • Intentional tradeoffs are separated from real defects.
  • The final branch passes tests and an independent PR review.

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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 first pass is broad but structurally weak
Give Opus the goal, constraints, repository context, and plan requirement, then stop when the feature is testable rather than polishing indefinitely.
Codex produces an unprioritized list of theoretical concerns
Ask for severity, evidence, affected paths, likely impact, and the smallest safe remedy for each finding.
The models repeatedly undo each other
Record accepted decisions and intentional tradeoffs, then give both models the same constraints before another pass.
The review itself becomes proof of correctness
Run repository checks and independent review after implementation; treat model feedback as hypotheses until verified.

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