Which came first, the JSON or the egg? Best practice in AI Video prompting.

As generative AI Video becomes an integral part of our creative and workflow processes, I've been increasingly focused on how we can refine our prompting methodologies for better, more consistent results. One approach I've found incredibly powerful is using JSON to structure prompts.

Take this transparent egg image, for instance.

Rather than a free-form text prompt, I used a detailed JSON structure to define every aspect: material, lighting, camera angle, and internal details.

The benefits are clear:

Readability: JSON's hierarchical structure makes complex prompts much easier to parse and understand at a glance.

Manipulability: Want to change the background or the object's material? It's a simple, targeted edit to a specific JSON field, not a search-and-replace mission in a block of text.

Repeatability: This is perhaps the most crucial. JSON provides a robust, repeatable framework. I can save this prompt, make minor tweaks, and confidently generate variations while maintaining core parameters. This consistency is invaluable for iterative design and scalable AI-driven workflows.

Moving forward, I believe structured prompting will be key to unlocking the full potential of generative AI, transforming it from a "hit-or-miss" experiment into a precise, repeatable tool.