When Google introduced the transformer architecture in its 2017 paper “Attention Is All You Need,” few realized how much it would help transform digital work. Transformer architecture laid the foundations for today’s GPTs, which are now part of our daily work in SEO and digital marketing.
Search engines have used machine learning for decades, but it was the rise of generative AI that made many of us actively explore AI. AI platforms and tools like custom GPTs are already influencing how we research keywords, generate content ideas, and analyze data.
The real value, however, is not in using these tools to cut corners. It lies in designing them intentionally, aligning them with business goals, and ensuring they serve users’ needs.
This article is not a tutorial on how to build GPTs. I share why the build process itself matters, what I have learned so far, and how SEOs can use this product mindset to think more strategically in the age of AI.
From Barriers To Democratization
Not long ago, building tools without coding experience meant relying on developers, dealing with long lead times, and waiting for vendors to release new features. That has changed slightly. The democratization of technology has lowered the entry barriers, making it possible for anyone with curiosity to experiment with building tools like custom GPTs. At the same time, expectations have necessarily risen, as we expect tools to be intuitive, efficient, and genuinely useful.
This is a reason why technical skills still matter. But they’re not enough on their own. What matters more, in my opinion, is how we apply them. Are we solving a real problem? Are we creating workflows that align with business needs?
The strategic questions SEOs should be asking are no longer just “Can I build this?,” but:
- Should I build this?
- What problem am I solving, and for whom?
- What’s the ultimate goal?
Why The Build Process Matters
Building a custom GPT is straightforward. Anyone can add a few instructions and click “save.” What really matters is what happens before and after: defining the audience, identifying the problem, scoping the work realistically, testing and refining outputs, and aligning them with business objectives.
In many ways, this is what good marketing has always been about: understanding the audience, defining their needs, and designing solutions that meet them.
As an international SEO, I’ve often seen cultural relevance and digital accessibility treated as afterthoughts. OpenAI offered me a way to explore whether AI could help address these challenges, especially since the tool is accessible to those of us without any coding expertise.
What began as a single project to improve cultural relevance in global SEO soon evolved into two separate GPTs when I realized the scope was larger than I could manage at the time.
That change wasn’t a failure, but a part of the process that led me toward a better solution.
Case Study: 2 GPTs, 1 Lesson
The Initial Idea
My…
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