My Perspective on GenAI

How I turned trial‑and‑error into a repeatable workflow

My experience with GenAI

Here’s my take after two years of living with generative AI in my engineering workflow:
GenAI is a powerful tool in the hands of experienced users. For everyone else, it can serve as an advanced search engine — but without care, it becomes a crutch that leaves you with code you can’t maintain.

I’ve learned this the slow way. My early attempts with CodeWhisperer were equal parts fascination and frustration. Every three months, I’d revisit AI coding assistants, hoping they’d grown up a bit. Now, in August 2025, I use GenAI nearly every day — but in a very deliberate way.

Where It Fits in My Work

Over the last two years, my AI‑assisted work has settled at about 20 % of my output. That slice covers:

• 💡 Ideation: Brainstorming topics or approaches when my brain is stuck in a loop.
• 🔧 Coding Support: Boilerplate generation, refactors, unit tests, documentation scaffolds.
• 📚 Research: Networking or domain background checks before diving deep.

Where I don’t use it: Complex production‑critical code or application business logic. Those still require humans to perform it.

The Case Study: AWS Student Management System

An example of this workflow in action is my AWS Student Management System, built for the MLT AI Course.

Before: The project was low‑priority, hacked together, and littered with rough scripts.
After: With AI assistance, I cleaned it up, added robust unit tests, and covered edge cases — all while multitasking with family life.

The caveat: The AI introduced multiple subtle bugs, all caught in code review (as diffs while it was working). Without my supervision, it would have broken functioning code and never caught it.

My Privacy Reality Check

Let’s get this straight: there’s no universal “safe” way to share code with GenAI. I treat anything I paste in as potentially visible outside my control. My mitigations:

• AWS SSO with 2FA
• Secrets in AWS Secrets Manager
• Prompts designed with exposure risk in mind

Your takeaway: Have your own plan. If you handle proprietary IP or sensitive data, think about what you share, where, and under what terms.

My Prompt Workflow Template

This is the repeatable method I wish I’d had when I started. It’s portable — use it with any assistant, IDE plug‑in, local model, or CLI tool.

1. Define the Idea
    - Describe your goal in detail, enough to set direction for the LLM.


2. Idea Refining / Clarifying Requirements
    - Create `tasks/initial_design.md`.
    - Let the AI ask up to 10 questions to refine the scope.
    - Record answers in the file.


3. Create a Plan
    - Based on the design, create `tasks/plan.md` with Goal, Prompt, Notes, Expected Output.
    - For code tasks:
        - Write unit tests for success criteria.
        - Run tests before considering a task “done”.
    - Update the plan as you progress.


4. Start a new session
    - Pass both `initial_design.md` and `plan.md` into the AI.

This extra Idea Refining step alone has saved me hours of back‑and‑forth correcting wrong assumptions.

My Role in the Loop

No matter the tools — lately, Microsoft 365 Copilot for chat history and mobile accessibility and AWS Q CLI for direct computer iteration (filesystem editing, runs scripts and more through Model Context Protocols (MCPs)) — my role stays the same:

• 🧭 Supervisor: Keep work aligned with the original goal
• 🏗 Architect: Make final design calls
• 🕵️ Code Reviewer: Catch hallucinations and brittle logic

When the AI veers off, I intervene early rather than letting it spiral.

The Risks and How I Handle Them

Thanks!

If you made it this far try my prompt workflow on your next project. Adapt it, and, improve it — but don’t skip the supervision. Let me know how it goes.

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MLT AI Course • AWS Student Management System repo