An effective AI development workflow involves treating models as a team of specialists. Use Claude as the reliable 'workhorse' for building an application from the ground up, while leveraging models like Gemini or GPT-4 as 'advisory models' for creative input and alternative problem-solving perspectives.

Don't rely on a single AI model for all tasks. A more effective approach is to specialize. Use Claude for its superior persuasive writing, Gemini for its powerful analysis and image capabilities, and ChatGPT for simple, quick-turnaround tasks like brainstorming ideas.

Move beyond simple prompts by designing detailed interactions with specific AI personas, like a "critic" or a "big thinker." This allows teams to debate concepts back and forth, transforming AI from a task automator into a true thought partner that amplifies rigor.

Building a single, all-purpose AI is like hiring one person for every company role. To maximize accuracy and creativity, build multiple custom GPTs, each trained for a specific function like copywriting or operations, and have them collaborate.

Don't view AI tools as just software; treat them like junior team members. Apply management principles: 'hire' the right model for the job (People), define how it should work through structured prompts (Process), and give it a clear, narrow goal (Purpose). This mental model maximizes their effectiveness.

The comparison reveals that different AI models excel at specific tasks. Opus 4.5 is a strong front-end designer, while Codex 5.1 might be better for back-end logic. The optimal workflow involves "model switching"—assigning the right AI to the right part of the development process.

Separating AI agents into distinct roles (e.g., a technical expert and a customer-facing communicator) mirrors real-world team specializations. This allows for tailored configurations, like different 'temperature' settings for creativity versus accuracy, improving overall performance and preventing role confusion.

For professional coding tasks, GPT-5 and Claude are the two leading models with distinct 'personalities'—Claude is 'friendlier' while GPT-5 is more thorough but slower. Gemini is a capable model but its poor integration into Google’s consumer products significantly diminishes its current utility for developers.

Instead of relying on a single, all-purpose coding agent, the most effective workflow involves using different agents for their specific strengths. For example, using the 'Friday' agent for UI tasks, 'Charlie' for code reviews, and 'Claude Code' for research and backend logic.