Here’s a management anti-pattern I see constantly: developers using one AI model for everything. It’s like hiring a brilliant systems architect and asking them to write CSS, review PRs, design your logo, AND refactor your database queries. Sure, they can do it. But should they?
I run a five-model AI development team. Each has a specialty. Each stays in their lane.
The Lineup
Claude (Anthropic) is my senior architect and technical writer. Complex system design, documentation, explaining why your microservices architecture is actually a distributed monolith—Claude excels here. When I need to think through IAM permission boundaries across twelve AWS accounts or draft an RFC, Claude’s my first call.
Kiro (AWS) is my IDE-native developer. It understands project context and handles spec-driven development with hooks and automated tests. It’s the disciplined engineer who follows requirements and writes tests before asking questions.
Amazon Q Developer is my AWS specialist. Infrastructure-as-code generation, debugging CloudFormation drift, explaining why your Lambda is timing out—Q speaks fluent AWS in a way generalist models don’t. It’s like having an SA embedded in your IDE.
ChatGPT (GPT-4) is my generalist and brainstorming partner. Quick questions, rough drafts, “what’s that Python library that does X” moments. It’s the developer who’s read a little bit about everything and can point you in the right direction fast.
v0 (Vercel) owns UI/UX. Need a React component that doesn’t look like it was designed by a backend engineer? v0 generates production-ready frontend code with actual design sensibility. Tailwind, shadcn/ui, responsive layouts—it speaks fluent frontend.
What Happens When You Use the Wrong Model
Ask Claude to rapid-fire autocomplete your code in the IDE? You’ll wait three seconds per suggestion while it contemplates the philosophical implications of your variable naming.
Ask GPT-4 to design your multi-region disaster recovery architecture? You’ll get something that sounds confident, cites no sources, and recommends services that were deprecated in 2022.
Ask v0 to architect your backend? You’ll get a beautifully styled API that stores user passwords in localStorage.
Ask Amazon Q to write your marketing copy? You’ll get a technically accurate description that somehow includes the phrase “leverage serverless paradigms” three times.
The Management Mindset
Treat AI models like a development team. You wouldn’t assign your database specialist to pixel-push your marketing site. You wouldn’t ask your junior developer to design your event-driven architecture.
Match the model to the task:
- Architecture and complex reasoning → Claude
- Spec-driven development and testing → Kiro
- AWS infrastructure and debugging → Amazon Q Developer
- Quick lookups and brainstorming → ChatGPT
- Frontend components and UI → v0
I context-switch between browser tabs and IDE integrations constantly. It feels inefficient until you realize the alternative: one model hallucinating its way through tasks it wasn’t optimized for.
Your AI team has specialists. Use them that way.