AI often seems a bit like magic. Type in a few words and out comes a response. Generally coherent and accurate. Occasionally brilliant and surprising.
But, of course, it’s not magic at all. Really, it’s about design. And you might be surprised to learn that the design required to get AI working effectively is not just technical. In many respects, it’s the kind of design that L&D professionals already understand, where the focus is on clarity, structure, and logic.
That realisation came into focus for me recently when I was exploring how three core technologies, Markdown, JSON, and Python, work together to create the kinds of custom GPT experiences that will underpin the PerformaGo platform.
Individually, they do very different things. But collectively, they provide a design pattern that’s surprisingly similar to the one we already use when designing learning.
Three Languages. One Design Flow.
Each of these three “languages” plays a specific role:
- Markdown gives content structure and readability: it’s the equivalent of clear instructional writing.
- JSON provides order and consistency: a framework for how elements connect and talk to one another.
- Python gives the system logic: it turns that structured information into meaningful action.
Together, they form a framework that embodies clarity, structure and action.
If that framework sounds familiar, it should. It’s the same logic that underpins effective learning design.
For example, when you design a piece of learning, you need to make the content clear and digestible. You need to give it a structure and flow that makes sense in the learner’s context. And then you ensure it supports action — that the learner can actually do something with it.
In that sense, using AI to design performance and designing a piece of learning are really two expressions of the same thinking process.
The Bridge Between Learning Design and System Design
As we move deeper into workplace where AI-assisted tools are more commonplace, the line between learning design and system design is blurring.
And that’s where PerformaGo’s design philosophy comes in. It’s being built on the same foundational ideas that make great learning work — structuring knowledge for clarity, connecting it and structuring it for meaning, and presenting it ready for action.
Why This Matters
For L&D, the lesson isn’t to become technologists. It’s to recognise that our design mindset already prepares us for this new world.
As AI becomes more embedded in workplace learning and performance, it’s not going to be replacing our design role, it’s going to be extending it. Because whether you’re designing a learning experience or an AI powered performance support system, the goal remains the same: to help people do their best work when it matters most.
Closing Thought
The “three languages” of AI, Markdown, JSON, and Python, may sound technical, but they’re really just another version of what L&D already knows.
When you combine clarity, structure, and logic, you create understanding. When you add purpose, you create performance. And that’s the design principle behind PerformaGo — using AI not to replace learning, but to bring the right guidance, at the right time, in the right way.
