AI Workflows & Design • 2026

Notebook LM Style Extraction: Turn Your Website Design into Consistent AI-Generated Slides

Extract your brand's design system using full-page screenshots. Turn visual website data into slide decks, design tokens, and AI-ready assets for Gemini 3.1 Pro—maintaining perfect visual consistency.

The Visual Extraction Workflow
Full-Page PNG
Notebook LM
Slide Decks
Design Tokens

Why Visual Extraction?

NotebookLM is a powerful synthesis tool that works directly from the materials you provide. While it handles text beautifully, its multimodal vision capabilities mean it can "see" your brand's design system when provided with high-quality screenshots.

Instead of manually documenting your design system, you can capture full-page screenshots of your website and upload them into Notebook LM. Because it can process visual hierarchy, it will extract:

  • Typography spacing and weight hierarchies
  • Color scheme dominance
  • Recurring layout patterns (like two-column feature cards)
  • Button styling and call-to-action rhythms

This grounding capability ensures Notebook LM doesn't hallucinate your branding—it reverse-engineers it directly from your live pixels.

Step 1: Capture Your Website

If you just paste a URL into an AI, it only reads the underlying HTML/text. It completely misses your CSS, padding, rounded corners, and brand identity. To capture the actual design, we need a high-fidelity image.

Use the embedded Snapshot Engine below to generate a full-page, high-res PNG of your target website. This will serve as the "ground truth" for Notebook LM.

Interactive Utility

Website Snapshot Engine

Enter your URL to generate a full-page PNG capture optimized for AI vision models.

Step 2: Upload to Notebook LM

Once you've downloaded the full-page PNG from the tool above, create a new notebook in Google's Notebook LM and upload the image as a source. If you have multiple key pages (like a homepage, a pricing page, and a feature page), capture and upload all of them.

Step 3: Prompting for Slide Extraction

Now that Notebook LM can "see" your brand, you need to prompt it to extract the design framework. Use this exact prompt:

Slide Extraction Prompt
"Analyze the uploaded website screenshots. Extract the core design system including layout structure, section hierarchy, typography patterns, and visual rhythm. Then, create a 10-slide presentation outline that strictly adheres to this visual style. Detail exactly how each slide should look, what font weights to use, and where elements should be positioned."

Notebook LM will generate a structured blueprint. Because it's looking directly at your UI, the resulting slide outlines will mirror your brand's unique cadence perfectly.

Step 4: Execute with Gemini 3.1 Pro

Once Notebook LM generates your highly-specific, visually-grounded text outline, you bring that outline over to an execution model like Gemini 3.1 Pro.

Gemini 3.1 Pro can take the strict layout rules generated by Notebook LM and output actual SVG graphics, slide-ready code, or structured layout files. Because the foundational structure was anchored by a real screenshot, the end result feels entirely "on-brand" and free from typical AI design hallucinations.

Frequently Asked Questions (FAQs)

1. Why use full-page screenshots instead of a direct URL?

URLs only provide HTML text to AI. Full-page screenshots allow vision models to see your CSS, spacing, typography, and actual design hierarchy.

2. Can Notebook LM read text inside the screenshot?

Yes, it uses advanced OCR and vision processing to read the copy and understand how that copy is styled.

3. Does this help with Gemini 3.1 Pro?

Yes. By using Notebook LM to create a strict text-based design system from the image, you can feed that system to Gemini to generate highly consistent graphics.

4. Can I capture sites behind a login?

The screenshot tool operates via a proxy. It can only capture publicly accessible URLs.

Conclusion: A New Era of AI Consistency

Visual style extraction represents a powerful shift in AI workflows. Instead of asking models to “guess” your brand based on text, you provide them with pixel-perfect truth.

By capturing your website, extracting style via Notebook LM, and feeding it into advanced models like Gemini 3.1 Pro, you create a closed-loop system of consistency. This method is incredibly effective—and for teams working across multiple AI tools, it is becoming the standard workflow.

CoreUX Design Systems

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