The Power of Structured Input

Master JSON prompting to get precise, reliable outputs from AI. Build reusable schemas, automate content at scale, and integrate seamlessly with APIs—without guesswork.

Try the Interactive Prompt Builder Why JSON Prompting?

AI Control: The Structured Output Revolution

Watch how structured prompts convert creative intent into dependable results—then copy the JSON patterns into your own workflows.

The Power of Precision: Mastering AI Prompts with Structured Data

Structuring your AI prompts with JSON offers transformative benefits for any AI application, giving you unprecedented AI control. To learn more about this approach, read our article on advanced prompt engineering techniques.

Consistency & Reliability

Eliminate guesswork. A defined schema ensures the AI returns data in the exact format you need, every single time, preventing errors and "hallucinated" fields.

Simplified Integration

Treat AI outputs like any other API response. Machine-readable JSON eliminates complex parsing, streamlining development and enabling robust, automated workflows.

Enhanced Control

Go beyond simple text. Define complex, nested instructions, specify data types, and enforce required fields to gain granular control over the AI's creative and analytical processes.

Ready to start your prompt engineering journey?

Try the Interactive Prompt Builder

Starter Templates

Copy, customize, reuse

Jumpstart your projects with these pre-built JSON prompts for various tasks.

Write a Blog Post

{
  "task":"write a blog post",
  "topic":"[topic]",
  "audience":"[audience]",
  "length":"500",
  "tone":"friendly"
}

Generate Social Posts

{
  "task":"generate social posts",
  "platform":"twitter",
  "count":5,
  "topic":"[topic]",
  "tone":"casual"
}

Create a Quiz

{
  "task":"create quiz",
  "source":"[text or URL]",
  "format":"mcq",
  "count":10
}

Create Notion Page

{
  "task":"notion_page",
  "title":"[Client Name]",
  "fields":{
    "email":"[email]",
    "status":"new"
  }
}

Interactive Prompt Builder for Text to Image AI

Experience the power of structured outputs. Use the controls below to build a detailed text to image AI prompt and see how it translates into a precise JSON object and a rich natural language command. This demonstrates how to organize visual ideas using the concepts of Mise-en-scène (what's in the scene) and Dispositif (the technology used).

Mise-en-scène (The Scene)

Subject

Setting

Mood

Lighting

Dispositif (The Camera & Tech)

Medium

Angle

Resolution / Detail

Generated JSON Prompt

Generated Natural Language Prompt

Learn More About Prompt Engineering

Putting It Into Practice: API Integration with JSON Prompts

See how to implement structured JSON prompts with popular AI platforms for API integration and AI workflow automation. To dive deeper into these topics, check out the official documentation for Google Gemini and OpenAI.

OpenAI's recommended approach is Structured Outputs, which guarantees the output matches a provided JSON schema. This is more robust than the older JSON Mode.

curl https://api.openai.com/v1/responses \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
  "model": "gpt-4o-2024-08-06",
  "input": [{"role": "user", "content": "Extract user details."}],
  "text": {
    "format": {
      "type": "json_schema",
      "name": "user_details",
      "schema": {
        "type": "object",
        "properties": {
          "name": { "type": "string" },
          "email": { "type": "string" }
        },
        "required": ["name", "email"]
      },
      "strict": true
    }
  }
}'

Google Gemini uses the responseSchema parameter within the generation configuration to constrain the model's output to a specific JSON structure. It's crucial not to duplicate the schema in the text prompt.

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash:generateContent" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-H 'Content-Type: application/json' \
-d '{
  "contents": [{
    "parts":[ { "text": "Extract recipe details." } ]
  }],
  "generationConfig": {
    "responseMimeType": "application/json",
    "responseSchema": {
      "type": "OBJECT",
      "properties": {
        "recipeName": { "type": "STRING" },
        "ingredients": { "type": "ARRAY", "items": { "type": "STRING" } }
      },
      "required": ["recipeName"]
    }
  }
}'

DALL-E 3's API directly accepts a JSON payload to control image generation parameters like prompt, size, quality, and style.

curl https://api.openai.com/v1/images/generations \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
  "model": "dall-e-3",
  "prompt": "A photorealistic image of a cat wearing a tiny wizard hat",
  "n": 1,
  "size": "1024x1024",
  "quality": "hd",
  "style": "vivid"
}'

Stable Diffusion APIs accept a JSON body to control a wide range of parameters, including prompts, negative prompts, dimensions, and style presets.

curl -X POST "https://api.stability.ai/v2beta/stable-image/generate/core" \
-H "authorization: Bearer $STABILITY_API_KEY" \
-H "accept: image/*" \
-F "prompt=A majestic lion in a futuristic city" \
-F "output_format=png" \
-F "aspect_ratio=16:9" \
-F "negative_prompt=blurry, low quality" \
-F "style_preset=fantasy-art"

Detailed JSON Prompt Examples

These three examples demonstrate how to construct detailed JSON prompts using the principles of Mise-en-scène and Dispositif to create distinct images.

1. Epic Fantasy Landscape

This prompt is designed to create a grand, cinematic fantasy scene using specific artistic references to guide the AI toward a powerful, epic feeling.

{
  "prompt": "A majestic dragon soaring over a medieval castle at sunset.",
  "mise-en-scene": {
    "subject": "majestic dragon",
    "setting": "medieval castle at sunset",
    "mood": "epic",
    "lighting": "cinematic volumetric lighting"
  },
  "dispositif": {
    "medium": "digital painting",
    "angle": "wide angle",
    "resolution": "highly detailed, 8K"
  },
  "cultural-object": {
    "genre": "fantasy",
    "artist_reference": "by Greg Rutkowski",
    "reception": "trending on ArtStation"
  }
}

2. Sci-Fi Cyberpunk Portrait

This prompt focuses on a close-up shot of a futuristic character in a neon-lit urban environment to create a sharp, detailed, and gritty sci-fi aesthetic.

{
  "prompt": "A futuristic cyborg with glowing neon circuits.",
  "mise-en-scene": {
    "subject": "futuristic cyborg with glowing neon circuits",
    "setting": "in a dense cyberpunk alley at night",
    "mood": "mysterious",
    "lighting": "neon lighting, light rain"
  },
  "dispositif": {
    "medium": "3D render",
    "angle": "low angle portrait",
    "resolution": "sharp focus, photorealistic"
  },
  "cultural-object": {
    "genre": "cyberpunk art",
    "artist_reference": "by Beeple",
    "reception": "concept art masterpiece"
  }
}

3. Whimsical Children's Illustration

This example is designed to generate a playful, charming image suitable for a book or cartoon, emphasizing a specific medium and perspective.

{
  "prompt": "A whimsical robot sitting on a glowing mushroom.",
  "mise-en-scene": {
    "subject": "a whimsical robot with a friendly expression",
    "setting": "in a fantastical mushroom forest",
    "mood": "playful",
    "lighting": "soft, warm light"
  },
  "dispositif": {
    "medium": "illustration",
    "angle": "macro shot, eye level",
    "resolution": "highly detailed, watercolor style"
  },
  "cultural-object": {
    "genre": "children's book illustration",
    "artist_reference": "inspired by Studio Ghibli",
    "reception": "award-winning"
  }
}

Best Practices & Common Pitfalls

Tips and solutions for crafting effective and robust AI prompts for structured outputs. To dive deeper into these topics, read our full guide on prompt engineering.

Always start your prompt by explicitly telling the model to respond in JSON. Use clear directives like "Respond with valid JSON only. Do not include any explanation or extra text." This prevents the model from defaulting to a conversational, unstructured response.

LLMs excel at pattern matching. Providing a "few-shot" example of the desired JSON structure or defining a clear schema within the prompt dramatically increases accuracy. The model will copy the structure you provide.

Sometimes, models produce partial or invalid JSON. Always wrap your parsing logic in a try-catch block. A powerful solution is to feed the malformed output back to the LLM and ask it to correct the JSON structure—it's surprisingly effective.

JSON syntax (braces, quotes, whitespace) consumes more tokens than plain text. For very high-volume or cost-sensitive applications, consider if a simpler format like Markdown or delimited text could achieve the same goal with less overhead. Always engineer prompts for brevity where possible.