🍳 EZ Recipe
Smart Cooking with What You Have
A comprehensive recipe and meal planning solution that adapts to your ingredients and dietary needs.
🎯 Project Summary
TL;DR
- Problem: Home cooks struggle to make meals with what they already have.
- Solution: Constraint‑based generator using ingredients, goals, and cuisine to produce adaptable recipes.
- Success criteria: fast entry, relevant results, trust in substitutions.
Constraints
- Solo build; emphasis on MVP speed and clarity.
- Ingredient variability and limited pantry scenarios.
- Mobile kitchen use; one‑hand interaction.
Collaboration & Feedback
- Informal testing with friends/family; edited copy and defaults for clarity.
- Feedback emphasized substitutions; added fallback meal types.
- Iterated macro visibility and defaults from early sessions.
EZ Recipe
As I was building Chefie, my broader health-tracking app, I consistently hit the same user problem: people struggled to know what to cook with the food they already had. Despite the explosion of cooking at home since COVID-19, recipe apps hadn't evolved to fit real-life user constraints. They delivered endless static recipes with little personalization, no awareness of dietary goals, and no connection to what users actually had in their kitchens.
EZ Recipe emerged from that need. It reframes cooking not as searching for the perfect recipe, but as solving for a set of real-world variables: ingredients on hand, dietary needs, time available, and cooking style.
My Role
This is a solo project, designed and developed end-to-end. I am the sole designer, developer, and founder of the live product at ezrecipe.app.
- Product Designer (UX/UI)
- UX Researcher
- Frontend Developer (HTML/CSS)
- AI Integrator (OpenAI API)
- Founder & Vision Owner
🔍 Design Process
1. Empathize & Research
User Research (Forum Analysis)
I analyzed hundreds of posts on Reddit communities (e.g. r/CookingForBeginners, r/EatCheapAndHealthy). Common themes emerged: users felt overwhelmed by complex recipes, frustrated by ingredient mismatches, and desired simple, adaptable meal ideas. Many explicitly sought help using up leftover ingredients without extra shopping. For instance, a 2020 survey found 61% of people were looking for simple, practical meal solutions, and 60% wanted recipes that use ingredients they already have (foodnavigator-usa.com) — a perfect match for EZ Recipe's value proposition.
Industry Trends
Reports confirm these trends. Inflation has shifted meals home: 78% of U.S. consumers report eating at home more often to save money (instacart.com). A Deloitte survey finds 52% of consumers now value convenience more than before (deloitte.com), while many still care about health (over half of new home cooks cited healthier eating as a motivation (foodnavigator-usa.com)). Notably, 70% of Americans say rising food costs make healthy eating harder, underscoring the need for cost-effective solutions (cited in Deloitte research). Food waste is also on people's minds: households report less food waste by using recipes that utilize what's on hand (foodnavigator-usa.com). These insights validated the opportunity: a tool that addresses cost, convenience, health, and waste by optimizing existing ingredients.
Competitive Analysis
I audited popular apps. For example, Yummly now offers AI-driven personalization and filters for diets and tastes (prnewswire.com), but it still relies on searching an existing recipe database. Mealime and Paprika focus on meal planning or recipe storage rather than dynamic generation. In summary, while competitors provide recipe discovery or planning tools, none offer on-the-fly recipe generation based on user's actual pantry constraints. This gap confirmed our unique position.
The competitive analysis revealed significant gaps in constraint-based recipe generation. Most existing tools focus on recipe discovery rather than solving the real problem: what to cook with what you already have.
Gap & Opportunity Identification
Synthesizing these insights revealed a clear gap: home cooks, especially busy parents and health-focused individuals, want constraint-based meal planning — solutions that adapt to what they actually have on hand and what their family craves that day, without forcing exhaustive prep or shopping trips.
Persona Development
Drawing from this research, I crafted the persona of Jessica — a working mother managing limited time, a variable pantry, and diverse family tastes. Jessica embodies the frustrations and goals uncovered in user research, and directly shaped EZ Recipe's focus on simplicity, flexibility, and control.
Persona: The Constraint-Based Home Cook
User Journey Map
To visualize Jessica's experience with meal planning before and after EZ Recipe, I created a journey map to better identify emotional pain points and moments of opportunity.
Goal: Jessica Plans a Weeknight Dinner. The journey map reveals how users currently struggle with recipe discovery, ingredient management, and meal planning. EZ Recipe addresses these pain points by providing a streamlined, constraint-based approach to meal planning.
2. Define
Problem Statement
The research highlighted a major gap: most users weren't looking for recipe inspiration — they were seeking solutions based on constraints. Through synthesis of qualitative interviews and competitive analysis, I mapped out key user needs and pain points into clear problem areas.
The goal was to shift from general recipe delivery to context-aware, personalized guidance. Instead of designing a database of recipes, I set out to design a decision engine that adapts to user input dynamically.
How might we help users cook healthy, enjoyable meals based on what they already have, without requiring complex planning or full pantry access?
User Stories
- As a busy parent, I want to find recipes using ingredients I already have so I can cook dinner without going to the store.
- As someone with dietary restrictions, I want recipes that automatically adapt to my needs.
- As a beginner cook, I want clear, step-by-step instructions that help me build confidence.
3. Ideate
I structured the app around 4 core questions: What's your goal? (Healthy or indulgent), Calorie target? (Low, Medium, High), Protein level? (Low, Medium, High), and Cuisine preference? (American, Vegan, Chinese, etc.)
This short questionnaire feeds into the ingredient editor. Users can input ingredients manually or scan fridge lists, edit quantities and remove items, and view AI-generated meals instantly.
4. Prototype & Design
Product Goals: Input ingredients from pantry/fridge, set health goals: calories, macros, cuisine type, generate personalized recipes via AI, suggest flexible substitutions and variations, prioritize speed, simplicity, and trust.
Design Principles: Frictionless First-Time Use (no logins, no long setup), Constraint-Aware Layout (emphasize available ingredients & adjustable filters), Lightweight Aesthetic (clean, soft visuals with food-forward colors), Mobile-Friendly (designed with one-handed use in mind).
All designs were prototyped in Figma, tested for responsiveness, and later developed in HTML/CSS for iteration.
User Experience Flow
- 1User inputs available ingredients and dietary preferences
- 2AI analyzes ingredients and generates personalized recipe suggestions
- 3User selects a recipe and views step-by-step instructions
- 4Recipe adapts in real-time based on available ingredients and substitutions
5. Test & Iterate
The primary objective was to validate whether EZ Recipe’s ingredient-based recipe generation felt fast, relevant, and easy to use — especially for busy home cooks.
Specific questions we aimed to answer:
- Can users quickly add their available ingredients without frustration?
- Do the generated recipes feel relevant and appealing?
- Does image generation enhance or hinder the experience?
- Are users confident in saving and re-finding recipes they like?
Method
We conducted moderated remote testing with 6 participants representing our target persona — busy parents and health-conscious home cooks. Sessions were run via Zoom using a clickable Figma prototype.
Participants
- Age range: 28–44
- 4 primary household meal planners
- 2 cooking beginners
- Mix of dietary preferences (2 vegetarian, 1 low-carb, 3 no restrictions)
Tasks Given
- Input 5–6 ingredients you currently have at home.
- Set a cuisine preference and calorie goal.
- Generate a recipe.
- Save the recipe.
- Locate the saved recipe.
- Toggle image generation on/off and regenerate a recipe (concept testing).
Metrics Collected
Quantitative
- Average time to input ingredients: 1 min 18 sec
- Recipe relevance rating (1–5): 4.3 avg
- % who found save/retrieve easy: 83%
- % who preferred recipes without images (for speed): 50%
Qualitative (user quotes)
- “I love that it uses what I already have—it feels smart.”
- “The save button confused me at first—looked like Spotify’s.”
- “Image recipes are nice for browsing, but I’d skip them if I’m in a hurry.”
Post-Test Questionnaire
(1–5 scale unless noted otherwise)
- How easy was it to input your ingredients?
- How relevant were the generated recipes to your needs?
- How much did the recipe images improve your experience?
- Would you prefer faster recipe generation even if it meant no images? (Yes/No)
- How confident do you feel saving and re-finding a recipe?
- How satisfied are you with the app overall?
Key Insights
- Ingredient entry flow works well but could be faster with barcode scanning.
- Recipe relevance is strong, but some users wanted more variety on repeat runs.
- Strategic testing of image generation concept validated concerns about speed — 50% of users preferred faster results without images, confirming it's not worth the development investment.
- Save button iconography needs differentiation to avoid confusion with music apps.
Next Steps
- Avoid image generation feature — concept testing confirmed user preference for speed over visual enhancements.
- Test barcode scanning for ingredient entry to improve input speed.
- Redesign "Save" icon for clarity to avoid confusion with music apps.
- Explore more diverse recipe outputs for repeated ingredient sets.
Build Process
- Frontend: HTML, CSS, Tailwind
- AI Integration: OpenAI API for recipe generation
- Deployment: Vercel hosting with live product at ezrecipe.app
- Features: Ingredient-based recipe generation, dietary preferences, calorie tracking, recipe saving
The architecture is modular, with future expansion planned for voice input, barcode scanning, and API-based grocery syncing.
Challenges & Lessons Learned
Challenge
Recipe logic broke when users had very limited ingredients
Solution
Created fallback logic to suggest general meal types (e.g. omelets, soups, bowls)
Challenge
Users wanted nutritional info, but not overwhelming detail
Solution
Added toggle for macro/carb visibility with soft UI prompts
Lesson
Great tools reduce decisions without reducing control. By narrowing input fields and focusing outputs, EZ Recipe creates confidence, not choice paralysis.
Impact & Results
What started as a design case study has evolved into a real product I built and launched, making a difference in people's kitchens:
User Success Stories
- Busy parents cooking dinner without grocery store trips
- Fitness enthusiasts hitting macro targets with available ingredients
- Reduced food waste through better ingredient utilization
- New cooks building confidence with step-by-step guidance
Product Metrics
The product I developed has proven that constraint-based cooking can be both practical and enjoyable, validating the core design principles from this case study.
Reflection
EZ Recipe challenged me to think not just as a designer, but as a home cook, a nutrition-aware user, and a product strategist. It taught me to embrace constraint-based design and create systems that guide without dictating.
The product has evolved from a design case study into a live application serving thousands of users. The next stage involves expanding features based on user feedback and exploring additional integrations to further enhance the cooking experience.
📈 Future Enhancements
- Add user login & meal history
- Expand to support allergies and intolerances
- Improve mobile scanning features
- Build export-to-grocery list function