Should You Prompt in JSON?

The Cinematic Prompt Format That Could Make or Break Your AI Video

I first came across structured JSON prompting for Veo 3 from my friend and Promise Studios founder Dave Clark, who shared how he was formatting detailed JSON blocks to guide the look and feel of generative clips throughout a film project.

It led me into a fast-moving conversation where creators are experimenting, comparing results, and debating whether this kind of structured format makes a real difference.

You’ll see creators calling it JSON prompting.

Some are using actual JSON objects sent through APIs. Others are writing text that looks like JSON to stay organized.

And some argue that it doesn’t matter much at all, because Veo often does whatever it wants anyway.

That last part isn’t wrong.

What Is the JSON Prompt Method?

This workflow is built around a single idea: use a language model to generate your visual prompt in JSON format, structured for AI video generation tools like Google Veo 3.

Here’s how it works in practice:

1. You write or extract a short scene or beat from your script.

2. You give that to an LLM (like ChatGPT or my custom GPT).

3. The model returns a fully-structured JSON block, with scene descriptors and precise actions.

4. You paste that block into Veo (or any generator) and get a visually precise, tonally consistent result.

Each JSON block includes all the key cinematic details:

• Camera composition and motion

• Subject appearance and behavior

• Scene location and lighting

• Ambient audio, voice, tone

• Color and film texture

• Dialogue and subtitle control

You can think of each block as a virtual shot design document, but instead of just describing the idea, you’re formatting it in a way the model can immediately act on.

More importantly: you’re still the director.

You decide the mood, the camera style, the look of the character, the texture of the light.

The LLM just helps you write it out in language the model can understand. It’s a collaboration between your eye and the machine’s syntax.

So Where Do I Land In This Debate?

In my own tests, I’ve seen structured JSON prompts outperform natural language, but also the other way around.

Structure matters, but format is flexible. I don’t believe JSON is a magic key.

I do believe good prompt structure—clear, consistent, thoughtfully layered—makes a difference.

Whether that’s done in bracketed blocks or crafted language doesn’t matter as much as how well the prompt communicates your intent.

What Does a JSON Prompt Actually Looks Like?

Here’s an example using structured JSON-style formatting: