Multi-AI Code Review Collaboration Framework

A systematic approach to getting diverse AI perspectives on code, then synthesizing insights into actionable improvements.

When to Use This

• Complex codebases where blind spots are likely

• Production/live trading code where reliability is critical

• Architecture decisions with trade-offs

• When you want to validate Claude's recommendations

• Before major refactoring or deployment

The Process

Phase 1: Initial Review (Claude)

• Claude reviews code and documents findings

• Creates context prompt for external AI

• Identifies platform-specific constraints external AI must understand

Phase 2: External AI Review

• User pastes prompt to external AI (Gemini, DeepSeek, GPT, etc.)

• External AI provides structured review

• User brings response back to Claude

Phase 3: Synthesis & Debate

• Claude evaluates external AI's points

• Categorizes into: Valid, Partially Valid, Invalid

• Explains reasoning for each categorization

• Creates response prompt for user to continue dialogue

Phase 4: Consensus

• Continue rounds until agreement reached

• Document final action plan

• Prioritize fixes by risk and effort

Prompt Templates

Template A: Initial External AI Request

[AI NAME] CODE REVIEW REQUEST - [PROJECT TYPE]

I need a comprehensive code review. This code [CRITICAL CONTEXT - e.g., "runs on live funded accounts"]. Please review thoroughly and respond in a format I can share with another AI for collaborative discussion.

CRITICAL PLATFORM CONTEXT (Read First)

[List platform-specific constraints that might not be obvious] [List what IS and ISN'T possible on this platform] [Explain why certain "standard" patterns don't apply]

REVIEW SCOPE

Please analyze:

1. Logic & Correctness - [specific concerns]
2. Risk Management - [specific concerns]
3. Performance - [specific concerns]
4. Reliability - [specific concerns]
5. Code Quality - [specific concerns]
6. Scalability - [planned expansion, multi-instance needs, performance at scale]
7. Future Updateability - [extension points, configuration extensibility, technical debt, breaking change risks]

FUTURE ROADMAP (if applicable)

[Describe planned features, scaling needs, and future requirements so the reviewer can assess how well the current architecture supports them]

THE CODE

[Include full code or key sections]

RESPONSE FORMAT

Structure your response as:

[AI NAME] CODE REVIEW - ROUND 1

🔴 CRITICAL ISSUES (Must Fix)

🟡 IMPORTANT CONCERNS (Should Fix)

🟢 MINOR SUGGESTIONS (Nice to Have)

✅ WELL IMPLEMENTED

🔮 SCALABILITY ASSESSMENT

🔧 FUTURE UPDATEABILITY ASSESSMENT

❓ QUESTIONS / CLARIFICATIONS NEEDED

📋 PRIORITIZED ACTION PLAN

EXISTING FINDINGS (if any)

[Include prior AI findings so new AI can confirm/challenge]

Template B: Response to External AI

CLAUDE'S RESPONSE TO [AI NAME] - ROUND [N]

✅ FULL AGREEMENT

[Points we agree on completely]

🤝 CONCESSIONS & MODIFICATIONS

[Points where Claude adjusts position with explanation]

🛡️ POINTS I STILL MAINTAIN

[Disagreements with detailed reasoning]

🔍 NEW OBSERVATIONS

[Anything new Claude notices based on discussion]

📋 UPDATED ACTION PLAN

[Current consensus on what to fix]

🤝 CLOSING QUESTION

[Ask if they agree or have remaining concerns]

Template C: Final Consensus Summary

MULTI-AI REVIEW CONSENSUS

Participants: [List AIs involved] Code Reviewed: [File/project name] Date: [Date]

AGREED FIXES (In Priority Order)

EXPLICITLY REJECTED SUGGESTIONS

VERIFIED AS CORRECT

• [Item 1 that was reviewed and confirmed good]
• [Item 2]

OPEN QUESTIONS FOR FUTURE

• [Any unresolved items to revisit later]

Best Practices

• Always provide platform context - External AIs apply generic patterns without knowing constraints

• Be specific about what CAN'T be done - Prevents suggestions for impossible approaches

• Request structured responses - Makes synthesis easier

• Track rounds - Label each exchange for clarity

• Document consensus - Final agreement should be explicit

• Implement incrementally - Test each fix before moving to next

Reference Files

• references/prompt-templates.md

• Copy-paste ready templates

• references/platform-contexts.md

• Pre-written context blocks for common platforms

• references/synthesis-checklist.md

• How to evaluate external AI suggestions