AI Prototyping Mobile-First Prompt Engineering

NutriSwap: Small Changes, Big Impact

My goal with this project was to explore rapid prototyping with AI tools to create a mobile-first nutrition web app, demonstrating efficient workflows and prompt engineering techniques.

Project Overview

Challenge

How might we leverage AI tools to develop an idea, rapidly prototype, and iterate on an app concept to meet regular people where they are with nutrition?

Approach

Use a combination of AI-powered tools, strategic prompt engineering, and mobile-first design principles to create a comprehensive prototype in record time.

Project Details

Duration: 3 weeks
Role: Solo Designer, Experimenter
Tools: Cursor, v0.dev, Claude, ChatGPT
Focus: AI Prototyping

Design Process

01

AI-Powered Exploration & Research

Brainstormed the core concept and used AI tools to help refine the idea; prompted AI to scan and summarize common features in existing nutrition-tracking apps.

02

Mapping User Flow & Prompt Engineering

Sketched a simple user journey from onboarding to ingredient editing and smart swap suggestions; developed prompts for producing initial wireframe concepts, iterating on prompt structure for optimal results.

03

Rapid Prototyping & Iteration

Generated screen flows, reviewed, and iterated on the wireframes with AI, which became a sounding board, letting me test and adjust ideas rapidly.

Key Learnings

AI as a Design Partner

AI tools work best when treated as collaborative partners rather than replacement tools. Strategic prompting and human oversight are crucial for quality output.

Mobile-First Efficiency

Starting with mobile constraints and using AI to rapidly iterate on responsive behaviors significantly accelerated the design process.

Prompt Iteration

The quality of the output is heavily dependent on the quality of the input. Context, curation, and guidance are crucial for working with AI.

Quality Control

Maintaining design quality requires careful curation of AI output and strategic human intervention at key decision points.

Results & Impact

Faster prototype development compared to traditional methods

Design iterations completed in minutes or hours rather than days

AI tools integrated into end-to-end design process

Next Steps

This prototype served as a proof-of-concept for AI-accelerated design workflows. Following this prototyping exercise, I downloaded the codebase created by v0.dev and used Cursor to build out the app.

Building with the help of AI tools also allowed me to quickly iterate on design decisions, prompt engineer functionality, and even test the app with simulated users. One of my learnings from this simulated user testing was that users would like to incorporate dietary needs into the app, such as allergies or other preferences.