Consumer • AI • 2025

EZ Recipe

Constraint-based recipe generation using ingredients, goals, and preferences.

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TL;DR

Problem: Recipe apps assume you’ll shop for the recipe. Home cooks need meals that fit what’s already in their kitchen, their time, and their goals.
What I did: As solo founder, designer, and full-stack developer, I researched, designed, and shipped an AI recipe generator built around six constraint questions in 2 months.
Outcome: Live at ezrecipe.app · 400+ recipes generated · 4.5/5 user rating · 67% faster perceived wait after a testing-driven fix.

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.

Home cooks consistently struggle to figure out what to make with what they already have, and most recipe apps still assume you’ll shop for the recipe rather than cook from your pantry.

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

Jessica, 36, is a working mother of two who wants to eat better, save money, and waste less food without planning elaborate meals.

Persona: Jessica Rhomes, 36, working mother of two in California. Goals: cook healthy meals without constant grocery trips, stay on budget, reduce food waste. Frustrations: recipe apps assume she has everything on hand; time wasted scrolling irrelevant recipes. Needs: a fast way to turn fridge and pantry leftovers into full meals.

User Journey

Mapping Jessica's meal planning experience.

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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.

All work Next: Versum Health →