My 2025 AI Development Workflow: Claude, Codex, Gemini & Google AI Studio

My AI Development Workflow 2025: How I Combine Claude, Codex, Gemini, and Google AI Studio

The future of software development doesn't lie in a single AI tool – but in the intelligent combination of several specialized models.

In this article, I'll show you my personal workflow, which I use daily to develop, plan, and visualize projects.

Why Multi-AI Instead of a Single Tool?

Every AI model has its strengths and weaknesses. Claude Code excels at coding, Gemini at image generation, Veo at videos.

Instead of relying on a "jack-of-all-trades" tool, I specifically use the best tool for each task – and even have the models check each other's work.

The result: Better code quality, faster iteration, and more creative solutions.

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My IDE Setup: Antigravity

As a foundation, I use Antigravity as my primary development environment.

What makes Antigravity special:

• Claude Code in the Terminal – Directly integrated for seamless coding
• Claude Code VSCode Extension – For context-aware code completion
• OpenAI Codex Extension – As a second opinion and debugging partner

This combination gives me the best of both worlds: Anthropic's deep code understanding and OpenAI's rapid iteration.

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Claude Code: The Heart of My Development

Claude Code is my primary tool for all coding and development tasks.

Strengths of Claude Code:

• Multi-File Refactoring – Understands complex codebases and can make comprehensive changes
• Context Awareness – Keeps the entire project context in mind
• Agentic Workflows – Can independently research, plan, and implement
• MCP Integration – Connects to external tools and data sources

Typical Use Cases:

• Implementing new features
• Performing code reviews
• Planning complex refactorings
• Developing API integrations
• Writing tests

Claude Code is particularly strong when it comes to interconnected, complex tasks that require a deep understanding of the codebase.

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OpenAI Codex: Debugging & Plan Validation

I use OpenAI Codex specifically for two main purposes:

1. Debugging

When I encounter a stubborn bug, I get a second opinion from Codex.

Often, another model sees problems from a different perspective.

2. Plan Validation ("Confirmation AI")

Before implementing a major implementation plan, I have Codex review the plan:

"Here is my plan for Feature X. Do you see any problems
or areas for improvement?"

This second opinion has saved me from costly mistakes several times.

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The "AI Model Ping Pong" Strategy

One of my most effective techniques is AI Model Ping Pong – the targeted back-and-forth between different AI models.

How it works:

1. Claude Code creates a first draft (code, plan, architecture)
2. Codex reviews the draft and provides feedback
3. Claude Code improves based on the feedback
4. Optional: Further iteration until the result is optimal

Why it works:

• Each model has different training data and perspectives
• Errors of one model are often recognized by the other
• The result is more robust than from a single model
• Similar to code reviews among humans

Real-world example:

1. Claude Code: "Here is my implementation for the
   Authentication system..."

2. Codex Review: "The approach is good, but I see a
   potential race condition problem with..."

3. Claude Code: "Good point! Here is the improved
   version with Mutex-Lock..."

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GitHub Spec-Kit: Structured Feature Planning with AI

A game-changer in my workflow is GitHub Spec-Kit – an open-source toolkit for Spec-Driven Development.

Instead of directly jumping into coding ("Vibe Coding"), I first write detailed specifications, which then serve as the basis for AI-powered implementation.

What is Spec-Driven Development?

The philosophy: Specifications become executable artifacts.

Instead of writing vague prompts like "Build me a login system", I define precisely what the system needs to be able to do – and the AI then implements this spec in a structured way.

The Spec-Kit Slash Commands

After initialization, the following commands are available:

My Spec-Kit Workflow

1. Create CONSTITUTION

• What principles apply to the project?
• Which technologies and patterns do we use?
• What are no-gos?

2. Write SPECIFY

• What should the feature be able to do?
• What user stories are there?
• Which edge cases need to be considered?

3. Develop PLAN

• How do we implement this technically?
• What architectural decisions do we make?
• What dependencies do we need?

4. Generate TASKS

• Break down tasks into small, actionable steps
• Set priorities
• Identify dependencies between tasks

5. Execute IMPLEMENT

• AI implements based on the spec
• Structured, predictable results
• Less "Vibe Coding", more precision

Why Spec-Kit has revolutionized my workflow

Before (Vibe Coding):

Prompt: "Build me a User-Authentication-System"
→ AI improvises
→ Unpredictable results
→ A lot of rework needed

After (Spec-Driven):

Spec: "Authentication-System with the following requirements:
- OAuth2 with Google and GitHub
- Session-based with Redis
- Rate-Limiting: 5 attempts/minute
- 2FA optional via TOTP
- Password-Reset via Email
- Audit-Log for all Auth-Events"

→ AI has clear specifications
→ Predictable, complete implementation
→ Less iteration needed

Spec-Kit + AI Model Ping Pong

The combination of Spec-Kit and my ping-pong strategy is particularly powerful:

1. Write Spec with Claude Code – Formulate detailed requirements
2. Have Codex review Spec – Identify gaps and ambiguities
3. Improve Spec – Based on feedback
4. Create Plan with Claude Code – Plan technical implementation
5. Validate Plan with Codex – Check alternative approaches
6. Start Implementation – With a solid foundation

Supported AI Assistants

Spec-Kit works with virtually all modern AI coding tools:

• ✅ Claude Code
• ✅ GitHub Copilot
• ✅ Cursor
• ✅ Windsurf
• ✅ Google Gemini
• ✅ OpenAI Codex

Three Scenarios for Spec-Kit

Quick Start with Spec-Kit

# Installation
npx specify init

# Or install persistently
npm install -g specify

# Initialize project
specify init my-project

# Then use the Slash Commands
/speckit.constitution
/speckit.specify
/speckit.plan
/speckit.tasks
/speckit.implement

Spec-Kit has fundamentally changed the way I plan and implement features. Instead of "Vibe Coding", I now work with precise specifications that lead to predictable, high-quality results.

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Gemini: Image Generation & 3D Illustrations

For visual assets, I use Google Gemini.

Areas of Application:

• Hero Images for blog posts and landing pages
• 3D Illustrations for technical concepts
• Icons and UI elements in a consistent style
• Infographics for complex workflows

Workflow with Gemini:

1. Describe the concept (detailed prompt)
2. Generate initial variations
3. Select and refine the best version
4. Export in various formats

Gemini's strength lies in its consistency – once a style is established, I can maintain it across many assets.

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Veo: Professional Video Generation

For video content, I rely on Google Veo.

Areas of Application:

• Product Demos and Feature Showcases
• Explainer Videos for complex concepts
• Social Media Content (short clips)
• Background Animations for Websites

Veo Workflow:

1. Create storyboard/concept
2. Prompt scene by scene
3. Generate and review clips
4. Post-processing and editing

Veo is particularly useful for rapid prototyping – before I invest in expensive video production, I first test concepts with AI-generated videos.

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Google AI Studio: Mockups & Animation Concepts

Google AI Studio is my go-to tool for creative concept work.

Website Mockups

• Landing Page Designs
• UI Concepts
• Responsive Layouts

Animation Concepts

• Scrollytelling Animations – How should content appear when scrolling?
• 3D Animations – Concept visualization before implementation
• Micro-Interactions – Button Hovers, Loading States, Transitions

Workflow:

1. Description of the desired animation/mockup
2. AI Studio generates visual concepts
3. Iteration until the desired result
4. Export as a reference for development

This saves an enormous amount of time in the concept phase – instead of iterating for hours in Figma, I quickly generate several variants and then decide.

Summary: My Tool Stack

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Conclusion: The Future is Multi-AI + Spec-Driven

The days when a single tool could handle all tasks are over.

I achieve the best results through:

1. Specialization – The best tool for each task
2. Specification – Spec-Kit for structured, predictable results
3. Validation – AI models check each other
4. Integration – Seamless workflow between the tools
5. Iteration – AI Model Ping Pong for optimal results

This workflow has doubled my productivity and significantly improved the quality of my work. The investment in learning multiple tools – especially Spec-Kit – pays off.

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Your Next Steps

1. Start with a Tool – Claude Code or Codex as a base
2. Install Spec-Kit – npx specify init for structured planning
3. Add Specialization – Gemini for images, Veo for videos
4. Establish Ping Pong – Have models review each other
5. Iterate your Workflow – Find out what works for you

The AI landscape is evolving rapidly – but the principles of Multi-AI usage and Spec-Driven Development will remain.

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Are you already using Spec-Kit or other structured planning tools in your AI workflow? Share your experiences in the comments!