Consumer • AI • 2025
EZ Recipe
Constraint-based recipe generation using ingredients, goals, and preferences.
Role
Founder
Product Designer
Full-Stack Developer
Timeline
2 months
Concept to launch
Team
Solo Project
End-to-end ownership
Skills
UX Research
Product Design
AI Integration
Overview
EZ Recipe reframes cooking as solving for real-world constraints: ingredients on hand, dietary needs, time available, and cooking style.
Users consistently struggled to know what to cook with what they already had. Despite the explosion of home cooking since COVID-19, recipe apps hadn't evolved to fit real-life constraints.
Problem
Home cooks struggle to make meals with what they already have. Recipe apps deliver endless static recipes with no connection to actual pantry contents.
Ingredient mismatches
Recipes require ingredients users don't have, forcing grocery trips or substitutions.
No constraint awareness
Apps don't consider dietary goals, time limits, or available ingredients.
Choice paralysis
Too many options without clear guidance leads to decision fatigue.
Food waste
Leftover ingredients go unused because there's no easy way to find recipes that use them.
"I have chicken, rice, and some vegetables. What can I make without going to the store?"
— Common user frustration
Research
I analyzed Reddit communities and industry research to validate the opportunity.
User Communities
- r/CookingForBeginners: Overwhelmed by complex recipes
- r/EatCheapAndHealthy: Need help using leftover ingredients
Market Signals
- • Growing demand for simple, practical meal solutions
- • Users want recipes using ingredients they already have
- • Increased focus on eating at home to save money
Competitive Analysis
Key Insight
Constraint-based cooking — Users want solutions that adapt to what they actually have, not recipes that require shopping trips.
Personas
User Journey
Mapping Jessica's meal planning experience.
Solution
A constraint-based questionnaire: input ingredients, set preferences, get AI-generated recipes that adapt to what you have.
Set Constraints
Time of day → Cuisine → Servings → Time limit → Calories → Food style
Input Ingredients
Add manually or scan → Edit quantities → View available options
Generate & Save
AI generates personalized recipes → Review substitutions → Save favorites
Design Principles
Frictionless first-time use: No logins, no long setup, just start cooking.
Constraint-aware layout: Emphasize available ingredients and adjustable filters.
Mobile-friendly: Designed for one-handed use in kitchen environments.
Design Process
From sketches to high-fidelity prototypes.
Initial Sketches
Exploring layout concepts and user flows with pen and paper.
Lo-Fi Prototypes
Testing the constraint selection and recipe generation flow.
Testing Insights
- • Dropdown menus preferred over text input for constraints
- • Ingredient input needed autocomplete suggestions
- • Recipe cards needed clear visual hierarchy
High-Fidelity Prototypes
Polished designs ready for development.
User Testing
Testing the live product with real users.
Image loading bottleneck
During family testing of the live platform, recipes were taking too long to generate because images were generating alongside the recipe text. We changed the flow so images load after the recipe is ready — reducing perceived wait time by 67% and improving satisfaction.
Design Decisions
Key decisions that shaped the product.
Why 6 constraint questions instead of fewer?
Users have diverse needs. Some care about calories, others about cuisine. Six questions with smart defaults let users customize without overwhelming first-time users.
Why AI generation instead of a recipe database?
Databases can't adapt to arbitrary ingredient combinations. AI generates unique recipes for any pantry, eliminating the "no results found" dead end.
Outcomes
400+
Recipes generated
4.5/5
User rating
67%
Faster perceived wait
85%
Easy save/retrieve
Learnings
What I learned building this product.
01 Great tools reduce decisions without reducing control
By narrowing input fields and focusing outputs, EZ Recipe creates confidence, not choice paralysis.
02 Constraint-based design works
Users want solutions that adapt to their reality, not recipes that require perfect conditions.
03 Real user testing reveals bottlenecks
Testing with family on the live product uncovered the image generation bottleneck that wouldn't have been obvious in prototype testing. Real usage surfaces real problems.
Next Steps
User login and meal history tracking
Expanded support for allergies and intolerances
Improved mobile scanning features
Export-to-grocery list function
Final Thoughts
EZ Recipe challenged me to think not just as a designer, but as a home cook, nutrition-aware user, and product strategist.
It taught me to embrace constraint-based design and create systems that guide without dictating. What started as a design case study is now a live application serving real users at ezrecipe.app.