Creating JSON Models for use in AI Prompts

Most people use Artificial Intelligence by simply asking for an image or a story in plain English. That works, sometimes. But it also leads to a familiar cycle: prompt → review → tweak → try again. Even if you get a great result on the first attempt, it’s almost impossible to recreate it later. A week from now, a month from now, or even the same day, the exact same prompt can produce a completely different result. This lack of repeatability makes narrative prompts unpredictable and ultimately disposable. In this video, instead of asking the AI for the finished image or narrative, we ask it to first create a structured model of the subject we want to render. These models are written in JSON, a language of structured data that is both easy to read and easy for AI to interpret. Why JSON? Because JSON forces Artificial Intelligence to stop guessing. It specifies attributes, constraints, visual features, and relationships. It separates content from style. It transforms prompting into a reliable, reusable system where the same character, object, or scenario can be rendered again and again, across styles, poses, scenes, and applications—with a high degree of accuracy. To demonstrate how this works, we build a JSON model of a fictional person named Alex. With only a few descriptive fields: age, build, clothing, features: we show how Alex can appear consistently across photorealism, illustration, surrealism, chalk, claymation, and even propaganda-style art. We also show Alex in motion and in context, riding a bicycle, playing hockey, and performing music. Finally, we render sequential images as a four-panel cartoon, something that narrative prompts struggle with because narrative prompting lacks continuity. Once our model exists as JSON, it becomes an asset. We can save it, upload it, modify it, or duplicate it. We can create new characters (like Marco) and have them interact. We can add new objects (like bicycles, instruments, or cars) and build composable scenes. The key point: we are no longer prompting for a single output, we are prompting for a reusable system. The method scales far beyond character design. In the book, we apply the same technique to baseball teams from the 1920s, automotive maintenance plans, psychological models, and publishing workflows. If you use AI for art, simulation, storytelling, prototyping, analysis, or automation, structured prompting gives you control instead of randomness. It replaces improvisation with engineering.