My AI Workflow—Prompting, Generating, and Curating: A Paradigm Shift

As I walk my dog, I’m also drafting this article. I’m talking through ideas out loud, and AI is helping me organize them in real time, offer feedback, and start putting the pieces together. By the time I get back, most of the structure is already in place. Then my role shifts. I read what has been created, add prompts where something needs to be adjusted, regenerated, or clarified, and begin curating what is actually worth keeping. That is my process now.

This way of working—prompting, generating, and curating—has changed how I think about work itself. These tools do more than speed things up. They open the door to ideas that once demanded more time, more manpower, or more budget. To me, the bigger shift is this: AI is not simply taking over jobs. It is changing how we define our role in the process.

When ChatGPT launched in November 2022, I was teaching at Boca Code as a senior instructor. After using it for about a week, I knew it would challenge much of what I had spent the past 20 years developing and cultivating—all through a simple prompt. My instinct was not to compete with that, but to adapt to it quickly. It felt like one of those spark moments when you know something is about to change everything and reshape the paradigm. A little over three years later, it has already changed more than I imagined.

Quick Summary

  • AI is reshaping work through a cycle of prompting, generating, and curating, shifting the human role from creator to decision-maker
  • Prompting sets direction, generating accelerates output, and curation brings meaning to large volumes of content
  • AI removes the friction of starting, allowing faster iteration and more focus on refining ideas
  • The real value now comes from judgment—choosing what to keep, revise, or discard
  • Guardrails remain essential, as speed does not guarantee accuracy or reliability
  • This workflow expands what’s possible, but also requires responsibility in how we use, validate, and scale AI-driven output
Table Of Contents
  1. Quick Summary
  2. Prompting: Setting Direction
  3. Generating: Speed and Scale
  4. Curating: Making Sense of Abundance
  5. Guardrails: The Human in the Loop
  6. Where It All Comes Together
  7. FAQ
  8. Sources and Further Reading
  9. Looking Ahead: Building with Canada

Prompting: Setting Direction

Prompting is how we communicate direction to AI. It can be as simple as a question or as layered as a set of instructions, revisions, and constraints that shape what comes next. For me, prompting is how I set direction. At first, I used it mainly to generate something new, whether that was a draft, an image idea, or a starting point for code. Over time, it became much more than that. Now I use prompts inside the work itself, asking for fact-checking, alternate phrasing, stronger logic, new ideas, or a different tone.

In that sense, prompting feels a bit like live editorial notes inside the document. It lets me guide the work as it develops instead of waiting until the end. I use that same approach in development too, whether I’m working through structure, testing logic, or exploring different ways to implement something across my GitHub work.

A futuristic AR dashboard displaying active workflows and recent activity logs floating in front of a man walking through a downtown district.

Generating: Speed and Scale

This is where AI most clearly changes the pace of the work. Once the direction is in place, it can generate drafts, code, ideas, and variations almost instantly. Before, a lot of time went into pushing through the blank-page stage and building a rough first version from scratch. Now I can get to that first layer much faster. That speed matters because it gives me something to react to sooner, which means I can spend more of my energy shaping the work instead of only trying to begin it.

That shift is already showing up across industries. Reuters reported that IBM said its use of Adobe’s generative AI tools in marketing shortened some end-to-end creative processes from two weeks to two days. To me, that captures the larger point: AI changes the rhythm of work by making the first version faster to reach.

Curating: Making Sense of Abundance

Once AI can generate drafts, code, ideas, and variations so quickly, the challenge shifts. The question is no longer just how to make something. It becomes how to make sense of everything that has been produced. That is where more of my energy goes now. I am looking at the output, deciding what belongs, what needs revision, and what actually supports the larger story or system.

That is why curation feels like the right word to me. It is more than editing a sentence or cleaning up a snippet of code. It is about bringing direction to abundance. The comparison that makes the most sense to me is an exhibition. The meaning comes alive through how the pieces are selected, arranged, and woven together into a larger story.

Over-the-shoulder shot of a person interacting with a holographic display projected from a smartwatch, showing various AI processing stages in a downtown environment.

Guardrails: The Human in the Loop

AI is a brilliant tool, but the real strength comes from the coordination between AI and human judgment. Guardrails help shape the quality of the work, guide the process, and keep the final result grounded in responsibility. Facts still need to be double-checked. Content still needs to be reread. Code still needs to be reviewed and tested.

We have already seen why that matters in public-facing systems. McDonald’s ended its AI-powered drive-thru test with IBM in June 2024 after mixed results and recurring accuracy issues. To me, that reflects the same issue that can appear in writing and coding: fast output can be helpful, but fast is not the same as dependable.

Cinematic shot of a futuristic home office featuring a man using advanced AR technology and a transparent screen against a soft-lit room.

Where It All Comes Together

Back at my desk, I’m reading through what AI and I have shaped together. There were plenty of edits, adjustments, and back-and-forth decisions along the way. That is part of what makes this new way of working feel so meaningful to me. It is not only about moving faster. It is about thinking bigger. Instead of getting stuck in the granular question of how to produce something, I get to spend more energy on the larger idea behind it, how to express it, how to shape it, and how it fits into a bigger system. You can see that in this plant catalog project, where I used AI across content, coding, and video.

At the same time, I know that convenience and speed come with responsibility. We still have to move carefully, stay thoughtful, and pay attention to the consequences that come with these tools. That includes the infrastructure behind them. The Canada Energy Regulator has noted that energy demand from data centers is steadily increasing, with AI development as a significant factor, and that rapidly rising electricity demand from data centres is an emerging issue in Canada.

The fact that I can go for a long walk with my dog, think through ideas out loud, and come back with real progress already in motion still feels remarkable to me. As the world changes, we need to adapt with it—but responsibly. That means embracing what these tools make possible while staying aware of the systems, costs, and consequences that support their convenience.

FAQ

Sources and Further Reading

Looking Ahead: Building with Canada

As I prepare to relocate to Canada, I’m focused on continuing to build in a way that is both practical and meaningful. This project reflects how I approach my work—organizing clearly, building intentionally, and using AI to support the process rather than define it.

I’m interested in contributing to teams and systems where structure, collaboration, and real-world use matter—creating work that is not only functional, but genuinely useful to the people interacting with it.

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