The Multi-LLM Workflow: Using AI as a Team
Why I use four different AI models — and how they complement each other.
April 2026
People ask which AI model is best. Wrong question.
The right question is: which AI model is best for what? Because after building several products with AI collaboration, I've learned they're not interchangeable. They're complementary.
My Current Roster
Claude
My primary collaborator. Product documentation, code architecture, complex reasoning, and the voice of caution when I want to add "just one more feature." Claude knows when to push back. That matters.
Gemini
Visual thinking and design direction. Branding, design systems, wireframes. When I need to turn a messy brain dump into something visually coherent, Gemini gets it faster than the others.
ChatGPT
Edge cases and integrations. When I hit weird API behavior or need to debug something that's not quite working, ChatGPT often has a different angle. Also good for research when I need multiple perspectives fast.
Ollama (Local)
Running locally for privacy-sensitive work and RAG systems. When I don't want data leaving my machine, Ollama handles it. Slower, smaller models, but fully under my control.
The Workflow in Practice
A typical project might start with Gemini for initial design exploration. I'll share screenshots of inspiration, describe the feeling I want, and let it generate visual direction before I write any code.
Then I move to Claude for architecture. What should the database schema look like? How should the components be structured? What are the edge cases I'm not thinking about? Claude excels at this kind of structured thinking.
Different models have different strengths. Using one for everything is like having a team where everyone has the same specialty.
When I hit problems, I'll sometimes take the same question to multiple models. Not because I'm shopping for the answer I want — but because different perspectives reveal different approaches. ChatGPT might suggest a library Claude didn't think of. Gemini might visualize a flow that clarifies the logic.
The Context Challenge
The hardest part of multi-LLM workflows is context management. Each model has its own conversation. None of them know what you discussed with the others.
I handle this with a few strategies:
Project briefs. I maintain a brief document for each project that any model can read. Quick context load for whichever AI I'm working with.
Copy decisions, not conversations. When Claude makes an architecture decision, I don't paste the whole thread into Gemini. I summarize the decision.
The life prompt. My personal context document works across all models. Same background, same preferences, regardless of which AI I'm talking to.
Primary and secondary. One model is always the "lead" on a project. The others are consultants. This keeps things from getting chaotic.
When Multi-LLM Works Best
Not every project needs multiple models. Quick fixes, simple questions, routine tasks — stick with one. The context switching overhead isn't worth it.
Multi-LLM workflows shine when:
- You're doing something genuinely new (no established patterns to follow)
- You're stuck and need a fresh perspective
- The project spans multiple domains (visual design + code + writing)
- You want validation that an approach is reasonable before committing
- Privacy requirements mean some work must stay local
Thinking About AI as a Team
I came from twenty years of building products with human teams. The best teams weren't full of identical thinkers. They had different perspectives, different strengths, productive tension.
AI collaboration works the same way. Claude thinks differently than Gemini. That's not a bug — it's a feature. When they disagree, it forces me to think harder about why. When they agree, I'm more confident.
The goal isn't to find the one perfect AI. It's to build a workflow where different intelligences complement each other — including your own.
I'm still the product manager. I'm still the one making final decisions. But I've got a team of collaborators available 24/7, each with their own strengths, and the cost of consulting all of them is trivial compared to the value of multiple perspectives.
If You Want to Try This
Start small. Pick two models. Use one for research and ideation, another for implementation. Notice what each does well. Notice what frustrates you.
Don't try to optimize immediately. Let the workflow emerge from actual use. After a few weeks, you'll naturally know which model to reach for in different situations.
And remember: you're the integrating intelligence. The AI models don't coordinate with each other. You're the one who synthesizes their outputs into something coherent. That's your job. It's a good job.
The best team has different perspectives. AI collaboration is the same.
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