The Allegorical Illustrator

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

What I Built

I built The Allegorical Illustrator, an application that transforms abstract philosophical concepts into stunning, allegorical art pieces. The app allows users to select a philosophical dilemma (like "The Ship of Theseus") and an artistic style (like "Japanese Woodblock Print") and uses AI to generate a unique visual representation along with a detailed explanation.

This app was created iteratively using a series of prompts. The initial prompt was simply to build the application based on a descriptive image. From there, I added features through conversational requests like:

• "Implement a 'Share' button... that allows users to share the generated image and its explanation together as an image."
• "Add a guardrail that first verifies if a given concept is actually representing a philosophical concept."
• "Add a feature to display a history of previously generated allegories... stored client-side using localStorage."

A key feature of this app is its multi-step, AI-powered generation and validation pipeline, which uses different models for different tasks. It first validates user input, then generates a detailed image prompt and a philosophical explanation, creates the image using Imagen, and finally performs a multimodal check to ensure the image is a relevant allegory for the concept.

Demo

https://the-allegorical-illustrator-789616276303.us-west1.run.app/

My Experience

Working on this project was a fascinating exercise in AI-driven development. My key takeaway is the power of a multi-step, "chain-of-thought" approach to prompting for complex tasks. Instead of trying to do everything in one massive prompt, breaking the problem down into sequential, validated steps—input validation, content generation, image creation, and image validation—produced a far more robust and reliable application.

I learned a great deal about leveraging the specific strengths of different models. I used the powerful gemini-2.5-pro for the nuanced tasks of interpreting philosophy and crafting creative prompts, imagen-4.0-generate-001 for high-quality image generation, and the fast, multimodal gemini-2.5-flash for the validation steps. Forcing the models to return structured JSON using responseSchema was a game-changer, making the integration between the AI and the application's logic seamless and error-free.

What surprised me most was how effectively the AI could be used to build its own guardrails. Prompting the AI to act as a "validation expert" to check the user's input, and later as an "art critic" to validate its own image output, felt like a glimpse into the future of building safe and high-quality AI applications. The entire development process felt less like traditional coding and more like a creative collaboration, guiding an incredibly capable assistant to bring a complex idea to life.