I’m a heavy AI user, but I don’t just open ten tabs and throw the same question at every model I can find. My workflow is deliberate, and every model has a defined role in the process.
It’s not that the “AI buffet” style is wrong — in fact, this excellent guide is a perfect example of that approach. But my own style is more like a relay race: each model runs its leg, then hands off to the next, so no one’s doing a job they’re bad at.
Phase 1 — Discovery & Requirements (ChatGPT as the Architect)
When I’m starting something new, I begin with long back-and-forth Q\&A sessions in ChatGPT.
- The goal is to turn fuzzy ideas into clear, testable requirements.
- I’ll ask “what if?” questions, explore trade-offs, and refine scope until I have a solid first draft of requirements and functional specs.
Why ChatGPT? Because it’s great at idea shaping and structured writing — and I can quickly iterate without burning expensive tokens.
Phase 2 — Critique & Refinement (Gemini as the Critic)
Once I have a draft, I hand it over to Gemini 2.5 Pro.
- Gemini acts like a tough peer reviewer — it questions assumptions, spots gaps, and points out edge cases.
- I take Gemini’s feedback back to ChatGPT for edits.
- We repeat this loop until the document is solid enough to hand off to implementation.
This step makes the coding phase dramatically smoother — Claude Code gets a blueprint, not a napkin sketch.
Phase 3 — Implementation (Claude Code as the Builder)
With specs locked in, I move to Claude Code for the actual build.
- I prep the context using a tool like AI Code Prep GUI to include only the relevant files.
- Claude Code follows instructions well when the brief is crisp and the noise is low.
- This is where the investment in phases 1 and 2 pays off — Claude isn’t guessing, it’s executing.
Phase 4 — Specialist Consultations (Free & Budget Models)
If something tricky comes up — a gnarly bug, architectural uncertainty — I call in a specialist.
- For deep problem-solving: o3, GLM 4.5, Qwen3 Coder, Kimi K2, or DeepSeek R1.
- For alternate perspectives: Poe.com’s Claude 4, OpenRouter’s o4-mini, or Perplexity for research.
- The point is diagnosis, not doing the build work.
These models are often free (with daily credits or token grants) and help me avoid overusing paid API calls.
Why This Works for Me
- Role clarity: Each model does the job it’s best at.
- Lower costs: Expensive models are reserved for hard, high-value problems.
- Better output: The spec→critique→build pipeline reduces rework.
- Adaptability: I can swap out models as free-tier offers change.
Closing Thoughts
AI isn’t magic — you are.
The tools only work as well as the process you put them in. For me, that process is structured and deliberate. If you prefer a more exploratory, multi-tab style, check out this free AI coding guide for an alternate perspective. Both approaches can work — the important thing is to know why you’re using each model, and when to pass the baton.