JSON Style Prompts for Product Photos: The Complete Guide with Examples

Ever tried generating product images with AI and got wildly different results each time? You type "place product in modern office" and get everything from a futuristic spaceship to a 1950s cubicle.

There's a better way.

JSON prompts bring programming precision to creative AI, transforming chaotic results into consistent, scalable image generation.

This guide shows you exactly how to use structured prompting to create professional product images that convert.

What Is a JSON Prompt?

A JSON prompt (also called a JSON image prompt or JSON style prompt) is a structured set of fields that tells the model exactly what to generate or edit. Instead of one long sentence, you specify clear keys like environment, visual_style, composition, and constraints so outputs stay consistent across SKUs and channels.

Why JSON Prompts Are Revolutionizing AI Image Generation

Traditional text prompts are like giving directions to a tourist without a map. JSON prompts? They're GPS coordinates. You give AI a set of CLEAR instructions on how to edit your product image.

Here's what changes when you switch to JSON:

• Reproducible Results: Same prompt = same style every time
• Consistent Results: Generate 1000 variations without losing consistency
• Systematic Testing: A/B test specific elements scientifically. Change one element, see the impact.
• Multi-Platform Ready: Works with GPT‑Image‑1.0, DALL‑E, Midjourney, VEO3 for video, and Google Gemini (aka Nano Banana). See real-world example here.

Is the output necessarily better?

No.

JSON prompts are not a magical solution for getting better results.

Why use it then?

It helps you structure your prompt, and that makes a lot of difference.

When we write a big long text style prompt we often end up waffling and adding too many things.

With JSON prompts you give a better structure to your prompt, and that makes a lot of difference.

Also as mentioned above, it helps to test and iterate on your prompt.

JSON Prompt Structure for Product Photos

When editing existing product images with AI, you don't describe the product - it's already in your image. Instead, you describe the world around it. Here's the comprehensive structure:

{
  "description": "High-level overview of the final image",
  "environment": {
    "location": "Where to place the product",
    "surfaces": ["What the product sits on", "nearby surfaces"],
    "props": ["Objects to include in the scene"],
    "spatial_arrangement": "How elements are positioned"
  },
  "visual_style": {
    "aesthetic": "Overall visual approach (minimal/lifestyle/dramatic)",
    "mood": "Emotional tone of the image",
    "lighting": {
      "type": "natural/studio/mixed",
      "direction": "where light comes from",
      "intensity": "bright/soft/moody",
      "color_temperature": "warm/neutral/cool"
    },
    "camera": {
      "angle": "eye-level/high/low/45-degree",
      "distance": "close-up/medium/wide",
      "depth_of_field": "shallow/deep",
      "focal_point": "what's in sharp focus"
    },
    "color_palette": ["dominant colors", "accent colors"],
    "textures": ["surface qualities to emphasize"]
  },
  "composition": {
    "product_placement": "center/rule-of-thirds/golden-ratio",
    "product_scale": "percentage of frame",
    "negative_space": "how much empty space",
    "balance": "symmetrical/asymmetrical"
  },
  "constraints": {
    "preserve_product": true,
    "maintain_shadows": true,
    "maintain_reflections": true,
    "humans_in_image": false
  },
  "technical": {
    "resolution": "output quality",
    "post_processing": ["color_grading", "contrast_adjustment"]
  },
  "output": {
    "format": "png/jpg/webp",
    "size": "1024x1024",
    "variations": 3,
    "naming_pattern": "descriptive file naming"
  }
}

Key Elements Explained:

1. Description: Start with a clear, one-sentence vision of the final image. This helps the AI understand your overall intent.

2. Environment: Don't just list props - describe how they relate to each other and the product.

3. Visual Style: Break down lighting and camera settings like a photographer would. Be specific about mood and aesthetic.

4. Composition: Think like a designer - where does the product sit in the frame? How much breathing room?

5. Constraints: Tell the AI what NOT to change. This preserves product integrity.

6. Technical: Include post-processing preferences for consistent brand look.

ℹ️ Important:
When creating product image prompts, it's best to avoid including humans in the scene. Current AI models often struggle to generate realistic people, which can result in unnatural or distracting results.

To ensure your images remain focused on the product, use the constraint:

"humans_in_image": false

This tells the AI to avoid adding people, helping maintain product integrity and visual consistency.

Building Your First JSON Prompt: Step by Step

Let’s build a JSON prompt together for a wireless speaker, following the key elements of a great prompt:

Step 1: Description
Start with a clear, one-sentence vision of the final image.

{
  "description": "Bluetooth speaker in an active lifestyle setting"
}

Step 2: Environment
Describe the setting and how props relate to the product.

{
  "description": "Bluetooth speaker in an active lifestyle setting",
  "environment": {
    "location": "poolside deck",
    "surfaces": ["wet pool tiles", "wooden deck"],
    "props": ["towel", "sunglasses", "tropical drink"]
  }
}

Step 3: Visual Style
Specify mood, lighting, and camera settings for a professional look.

{
  "description": "Bluetooth speaker in an active lifestyle setting",
  "environment": {
    "location": "poolside deck",
    "surfaces": ["wet pool tiles", "wooden deck"],
    "props": ["towel", "sunglasses", "tropical drink"]
  },
  "visual_style": {
    "mood": "summer party vibes",
    "lighting": {
      "type": "bright sunshine",
      "direction": "top down with pool reflections"
    },
    "color_palette": ["aqua blue", "bright white", "tropical colors"]
  }
}

Step 4: Composition
Define where the product sits in the frame and the overall layout.

{
  "description": "Bluetooth speaker in an active lifestyle setting",
  "environment": {
    "location": "poolside deck",
    "surfaces": ["wet pool tiles", "wooden deck"],
    "props": ["towel", "sunglasses", "tropical drink"]
  },
  "visual_style": {
    "mood": "summer party vibes",
    "lighting": {
      "type": "bright sunshine",
      "direction": "top down with pool reflections"
    },
    "color_palette": ["aqua blue", "bright white", "tropical colors"]
  },
  "composition": {
    "product_placement": "center foreground",
    "breathing_room": "ample space around product"
  }
}

Step 5: Constraints
Tell the AI what NOT to change, and use constraints like "humans_in_image": false to avoid unwanted elements.

{
  "description": "Bluetooth speaker in an active lifestyle setting",
  "environment": {
    "location": "poolside deck",
    "surfaces": ["wet pool tiles", "wooden deck"],
    "props": ["towel", "sunglasses", "tropical drink"]
  },
  "visual_style": {
    "mood": "summer party vibes",
    "lighting": {
      "type": "bright sunshine",
      "direction": "top down with pool reflections"
    },
    "color_palette": ["aqua blue", "bright white", "tropical colors"]
  },
  "composition": {
    "product_placement": "center foreground",
    "breathing_room": "ample space around product"
  },
  "constraints": {
    "preserve_product": true,
    "show_water_resistance": true,
    "humans_in_image": false
  }
}

Step 6: Technical
Include any post-processing or output preferences.

{
  "description": "Bluetooth speaker in an active lifestyle setting",
  "environment": {
    "location": "poolside deck",
    "surfaces": ["wet pool tiles", "wooden deck"],
    "props": ["towel", "sunglasses", "tropical drink"]
  },
  "visual_style": {
    "mood": "summer party vibes",
    "lighting": {
      "type": "bright sunshine",
      "direction": "top down with pool reflections"
    },
    "color_palette": ["aqua blue", "bright white", "tropical colors"]
  },
  "composition": {
    "product_placement": "center foreground",
    "breathing_room": "ample space around product"
  },
  "constraints": {
    "preserve_product": true,
    "show_water_resistance": true,
    "humans_in_image": false
  },
  "technical": {
    "post_processing": "increase vibrancy, sharpen product edges",
    "output_format": "jpg",
    "output_size": "1024x1024"
  }
}

JSON Prompt Templates for Product Photos (Copy/Paste)

Use these ready‑to‑run templates for common e‑commerce needs.

White Background (Marketplace/Shopping)

{
  "description": "Studio packshot on clean white background",
  "environment": {
    "location": "seamless white backdrop",
    "surfaces": ["matte tabletop"],
    "props": []
  },
  "visual_style": {
    "aesthetic": "catalog / packshot",
    "mood": "neutral",
    "lighting": {
      "type": "soft studio",
      "direction": "45-degree key with soft fill",
      "intensity": "even",
      "color_temperature": "neutral"
    },
    "camera": {
      "angle": "eye-level",
      "distance": "medium",
      "depth_of_field": "deep",
      "focal_point": "product front"
    },
    "color_palette": ["white", "neutral grays"],
    "textures": ["matte"]
  },
  "composition": {
    "product_placement": "center",
    "product_scale": "70% of frame",
    "negative_space": "moderate",
    "balance": "symmetrical"
  },
  "constraints": {
    "preserve_product": true,
    "maintain_shadows": true,
    "maintain_reflections": true,
    "humans_in_image": false,
    "no_text_overlays": true,
    "no_brand_watermarks": true
  },
  "technical": {
    "resolution": "high",
    "post_processing": ["neutral color grading"],
    "output_format": "jpg",
    "output_size": "1200x1200"
  }
}

Lifestyle Variant (Ad/Organic)

{
  "description": "Product in a real lifestyle scene",
  "environment": {
    "location": "modern kitchen countertop",
    "surfaces": ["light quartz"],
    "props": ["fresh herbs", "open cookbook"],
    "spatial_arrangement": "props loosely framing the product"
  },
  "visual_style": {
    "aesthetic": "natural lifestyle",
    "mood": "bright and clean",
    "lighting": {
      "type": "window light",
      "direction": "from left",
      "intensity": "soft",
      "color_temperature": "slightly warm"
    },
    "camera": {
      "angle": "45-degree",
      "distance": "medium",
      "depth_of_field": "shallow",
      "focal_point": "product"
    },
    "color_palette": ["white", "sage", "natural wood"],
    "textures": ["stone", "linen"]
  },
  "composition": {
    "product_placement": "rule-of-thirds",
    "product_scale": "55% of frame",
    "negative_space": "room for ad copy (outside image if needed)",
    "balance": "asymmetrical"
  },
  "constraints": {
    "preserve_product": true,
    "humans_in_image": false
  },
  "technical": {
    "post_processing": ["subtle contrast boost"],
    "output_format": "webp",
    "output_size": "1600x1200"
  }
}

Editing Prompts: Turn One Product Photo into Many

Use these when you upload a source product photo in BackdropBoost (or similar). The JSON becomes the instruction body; the app handles the image.

Background to Pure White (keep shadows)

{
  "description": "Convert to clean white packshot while preserving form",
  "environment": { "location": "seamless white" },
  "visual_style": {
    "aesthetic": "catalog / packshot",
    "lighting": { "type": "soft studio", "intensity": "even" }
  },
  "composition": {
    "product_placement": "center",
    "product_scale": "70% of frame",
    "negative_space": "moderate"
  },
  "constraints": {
    "preserve_product": true,
    "maintain_shadows": true,
    "no_text_overlays": true,
    "humans_in_image": false
  }
}

Seasonal Lifestyle (Winter/Holiday)

{
  "description": "Seasonal lifestyle variant with winter decor",
  "environment": {
    "location": "cozy living room",
    "surfaces": ["wood coffee table"],
    "props": ["evergreen sprigs", "mug with steam", "soft blanket"]
  },
  "visual_style": {
    "mood": "warm and festive",
    "lighting": { "type": "window light", "color_temperature": "warm" },
    "color_palette": ["pine green", "cream", "brass"]
  },
  "composition": {
    "product_placement": "rule-of-thirds",
    "negative_space": "light"
  },
  "constraints": {
    "preserve_product": true,
    "humans_in_image": false,
    "no_brand_watermarks": true
  }
}

Flat Lay Grid (Social/UGC Feel)

{
  "description": "Minimal flat lay with soft shadows",
  "environment": {
    "location": "paper sweep backdrop",
    "surfaces": ["matte paper"],
    "props": ["matching accessories", "dried leaves"]
  },
  "visual_style": {
    "aesthetic": "minimal / editorial",
    "lighting": { "type": "soft studio", "direction": "top-down" }
  },
  "composition": {
    "product_placement": "grid layout",
    "product_scale": "60% of frame",
    "negative_space": "generous",
    "balance": "symmetrical"
  },
  "constraints": { "preserve_product": true, "humans_in_image": false }
}

Nano Banana Tips Applied to JSON

• Be explicit about subject, environment, style, lighting, and camera.
• Use clear negatives: "humans_in_image": false, "no_text_overlays": true.
• Keep product integrity: "preserve_product": true; don’t change brand marks/materials.
• Maintain consistent camera/lighting for series; vary one field per A/B test.
• Describe relationships, not just lists (e.g., "props loosely frame the product").
• Avoid logos/trademarks you don’t own; keep Shopping-safe details.

Advanced Tips from the Trenches

The 80/20 Rule

Focus JSON complexity on your top 20% products that drive 80% of revenue.

Version Control Your Prompts

When you are working with JSON prompts, you should version control your prompts.

This is a good practice to keep track of your changes and to be able to revert to a previous version if needed.

It also enables you to test and iterate on your prompt.

I also like to add a performance note to the prompt. So I know how it is performing.

{
  "prompt_version": "2.1",
  "last_updated": "2025-08-02",
  "performance_note": "3.2x CTR improvement over v1.0"
}

JSON Prompt Examples

Model Compatibility Notes

• Gemini (Nano Banana): handles text prompts and text+image edits; JSON works well as a structured text payload.
• DALL‑E / GPT‑Image‑1.0: treat the JSON as a style spec; some fields may be ignored.
• Midjourney: doesn’t parse JSON directly; convert fields into concise, comma‑separated text.

Conclusion: Turn Structure into Sales

Stop crossing your fingers every time you hit “generate”.
A battle-tested JSON prompt gives you:

1. Repeatability – one prompt, identical brand look across 1,000 SKUs.
2. Speed – loop through a spreadsheet, swap location or mood, and watch variations roll in.
3. Measurable lift – tweak a single JSON key, A/B test, double-down on what converts.
4. Version Control – track changes, revert to previous versions, and test new ideas.

If you’re serious about scaling creative testing for Google Shopping (or any marketplace), make JSON prompting the backbone of your image pipeline.

Copy the template above, plug in your product details, and start shipping fresh creatives today.

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FAQ

What is JSON prompting?

It’s the practice of structuring an image prompt as a JSON object (with keys like environment, visual_style, composition, and constraints) to get consistent, repeatable results across many products.

How do I build my first JSON prompt?

Start small. Define a clear "description" and add one or two keys such as "environment" and "visual_style". Generate a few images, review what works, then iterate.

Can I bulk-generate images with these prompts?

Absolutely. Because JSON is machine-readable you can feed the same structure—plus dynamic values from a CSV—into tools like BackdropBoost and render thousands of variations in minutes.

Do JSON prompts work across different AI models?

Yes. The overall schema stays the same. You may rename a field or two (e.g., Midjourney prefers camera_angle while Nano Banana doesn’t care) but 90 % of the prompt can be reused without changes.

Can I turn an image into a JSON prompt?

Yes. Use an image‑understanding model or your own analysis to describe an existing photo and map the description into your JSON schema. This helps you reverse‑engineer the style and then reproduce it consistently.

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