Color pedia Cats with Imagen API

This post is my submission for DEV Education Track: Build Apps with Google AI Studio.

What I Built

I built „Colorpedia Cats,“ a whimsical web app that explains complex topics through illustrated slideshows.

Core Functionality

• Input: Users enter a topic they want to understand (e.g., „How do neural networks work?“).
• Process: The app generates a fun, easy-to-understand story using tiny cats as a metaphor.
• Output: It creates a slideshow where each part of the story is paired with a vibrant, AI-generated cartoon illustration.

Technical Implementation

The key was using the responseSchema feature with the Gemini API to get structured JSON output.

• Prompt Strategy: A detailed prompt asks the model to return an array of „slides.“
• Structured Data: Each slide object in the array contains:
  • slide_text: A single sentence for the slide’s caption.
  • image_prompt: A detailed prompt for generating a matching illustration.

• Model Chaining:

  1. gemini-2.5-flash generates the story and image prompts in the specified JSON format.
  2. imagen-3.0-generate-002 uses the image_prompt from each slide object to create the unique, colorful illustrations.

Demo

Visit ▶️ Colorpedia Cats

The application provides a simple and delightful user experience:

• UI: A colorful and friendly interface invites users to input a topic.
• Loading State: The app shows its progress (writing story, illustrating slides) while generating content.
• Slideshow: The result is a horizontal slideshow, perfect for storytelling.
• Visual Learning: Each slide features a unique, cute cartoon-style illustration of cats demonstrating a concept, with a simple caption below.
• Interaction: Users can scroll through the slides to learn about the topic in a fun and visually engaging way.

My Experience

This project was a fantastic learning experience, highlighting several key aspects of building with Google AI.

Key Takeaways

• Power of responseSchema: This feature is a game-changer. It makes building reliable, multi-step AI applications much simpler by providing predictable, structured JSON, eliminating the need to parse messy, unstructured text.
• Effective Model Chaining: I was surprised by how smoothly the text generation model (gemini-2.5-flash) could produce high-quality, imaginative prompts for the image generation model (imagen-3.0-generate-002) in a single, efficient API call.
• The Art of Prompt Engineering: Fine-tuning the main prompt to establish the „tiny cats“ metaphor and a „fun, conversational“ tone was a creative and rewarding challenge.

Overall, this project demonstrated how a well-crafted prompt, combined with structured output, can turn a complex idea into a functional and delightful application.