When I realised I needed to understand more about JSON (aka JavaScript Object Notation) I imagined I would soon find myself deep down yet another technical rabbit hole. Just the name made it sound intimidating. But it turns out JSON is less about coding and more about clarity.
At its core, JSON is simply a way of putting structure around information so that both humans and machines can read, understand and exchange that information more readily. JSON’s basic structure consists of labels and their values. In other words, a way to identify and group information clearly and logically.
It’s that simple structure that makes JSON so powerful; it’s also what helps to make AI systems in general, and PerformaGo in particular, accurate and more reliable.
When Accuracy Matters, Structure Is Everything
If you’ve ever had an AI tool “hallucinate”, i.e., confidently producing an answer that’s completely wrong, the problem isn’t that AI is ‘dishonest’, it’s more likely about poor structure.
Most large language models work by predicting words, not storing facts. They’re brilliant at generating fluent language but can be unreliable when precision and consistency matter.
That’s where JSON comes in.
By giving information a predictable format — one that clearly defines what each piece of data means and how it relates to others — you create a stable reference point.
In other words, you make determinism possible: the ability to get the same correct answer every time the same question is asked. (By the way, I wrote about this whole issue from a more personal perspective in a PerformaGo Diary post).
A Simple Example
Let’s say we’re storing information about a company in JSON:
{
“name”: “Pacific Blue”,
“products”: [“PerformaGo”, “PerformaGo Light”, “PerformaGo Ultra”],
“founder”: “Andrew Jackson”
}
No ambiguity. No interpretation. It’s saved and stored, ready to be used whenever required. So, if an AI model needs to retrieve that data, it can’t invent something new — it must return what’s defined.
That’s how you move from “AI guessing” to “AI knowing.”
From Determinism to Dependability
In performance support, this kind of reliability is non-negotiable. If someone asks a question in the middle of a task — “What’s the correct version of this procedure?” — the system can’t improvise.
By using structured content behind the scenes, PerformaGo ensures that when accuracy matters, responses are deterministic; in other words, drawn from verified, structured data, not model prediction.
That’s the quiet engineering behind dependable AI. It’s what turns an intelligent assistant into a trusted partner.
Why You Don’t Need to Learn JSON
The beauty of JSON is its simplicity. You can read it with barely any technical knowledge. But actually, you’ll never need to. Because PerformaGo uses JSON under the hood to store, label, and serve information.
So, you get the benefit of structured, accurate, and reusable content without ever needing to touch what’s going on behind-the-scenes.
Structure as the Foundation of Trust
In learning design, structure has always mattered. It helps people make sense of information and apply it correctly. JSON takes that same principle and applies it to machines — helping them make sense of what we mean.
That’s why PerformaGo is being built around this idea: the more structured the foundation, the more reliable the performance. Because when you’re helping people in the moment of need, accuracy isn’t optional — it’s the whole point.
Andrew Jackson is the co-founder of Pacific Blue Solutions, founder of Pacific Blue AI and creator of PerformaGo — a platform that helps L&D teams turn learning into performance support using AI.
He writes about bridging design thinking, workflow learning, and AI precision.
