## Letting AI code while I do QA?

I am testing a structured workflow for AI‑assisted dev.

I’ve been in webdev for years, wearing many hats—webmaster, UI/UX designer, full‑stack developer, QA, DevOps, tech SEO, and project manager.

That also means I see myself as a crappy coder—but one who understands the full architecture of a developed app. That’s why I’ve been exploring coding assistants to help me create apps from scratch. My assumption? Let the AI handle the coding while I focus on QA.

![Directory Structure for my AI Assisted Dev](/content/uploads/2025/02/ai-assisted-dev-carmelyne.webp)
*Directory Structure for my AI Assisted Dev*

**The goal?** Bridge the gap between planning & dev—so AI coding assistants don’t just generate code in a vacuum but actually follow a structured dev process.

But even with all this structure, AI still struggles with consistency. I’m now figuring out how to handle cases where AI needs [to transpile code & follow prompts reliably](/exploring-context-and-continuity-in-ai-assisted-development-the-ai-dev-workflow-cli/).

As you know, coding assistants have limited contextual awareness, meaning they often:

❌ Miss dependencies
❌ Misinterpret instructions
❌ Lose track of previous logic

💡 I’d love to hear from others trying this approach!

- How do you get AI to transpile code reliably (e.g., Sudolang) across different languages or frameworks?
- What’s the best way to keep prompts structured so AI maintains consistency?
- Have you found an effective workflow to extend AI’s contextual memory for larger projects?

I’m just trying to figure this out too—curious to hear what’s worked (or hasn’t) for you.

**END_OF_REPORT** 🌿✨