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AI Recipe Generator - Project Information

  • Category: Web App
  • Ownership: End-to-end (Product, UX, Engineering)
  • Project date: June, 2025
  • Stack: OpenAI GPT-4o (image generation), Gemini API (recipe creation)
  • Github: https://github.com/sahil265wins/recipe-ai-reveal

Project Overview

Problem

  • International students, especially those new to the US, face significant challenges when cooking in an unfamiliar environment. After grocery shopping, they often have a mishmash of ingredients with little idea what meals can be made from them. This leads to choice paralysis, reliance on family for decisions, wasted food, and frequent frustration. There is currently no solution that lets users simply take a picture of their available ingredients and instantly receive relevant recipes. This gap increases decision fatigue for new cooks and busy professionals alike. An instant, photo-based ingredient-to-recipe tool would dramatically simplify the cooking experience.

    High Level Approach

  • AI Recipe Generator bridges the gap between discovery and action. Users quickly upload a photo of their available ingredients through a mobile-friendly web app. The system recognizes the ingredients and instantly generates a relevant recipe, or alternatively accepts manual text lists. This minimizes friction, eliminates guesswork, and empowers users to cook confidently with what they have.

    Narrative

    • International Student Post-Shopping: Priya opens the AI Recipe Generator, snaps a photo of her shopping bag, and instantly gets several recipe ideas using those ingredients—no need to call her mom.
    • Preventing Food Waste: Raj notices leftover veggies. The app suggests a stir-fry recipe, letting him use everything up before it spoils.
    • Fast Meal for Busy Professional: Anna uploads a photo of her fridge contents. The app recommends a 15-minute quick meal.
    • Independent Cooking Decisions: Users rely on the app for meal inspiration tailored to what’s on hand.
    • Future (Grocery Planning): At the store, a user inputs what’s at home and receives a short list of items to buy in order to make a complete meal.

    Goals

    • Primary Goals
      • Maximize recipe generation per user session.
      • Reduce decision fatigue and increase confidence.
      • Empower new cooks—especially international students.
      • Encourage ingredient utilization to reduce food waste.
      • Create a simple, trusted daily tool.
    • Secondary Goals
      • Drive recurring engagement (7-day and 30-day retention).
      • High photo-to-recipe conversion rates.
      • Technical groundwork for advanced personalization (dietary filters, journaling) in future versions.

    Metrics

    MetricDefinitionGoal/Guardrail
    North StarRecipes generated per userHigher is better
    DAUsDaily active usersMinimum week-over-week retention
    Retention Rate% active users over 7/30 daysHealthy trend; flag <15% 7-day
    Session TimeAvg. session time2+ minutes/session
    Photo→Recipe Conversion% photo uploads yielding a recipe>80% MVP, >90% target (less than 60% unacceptable)
    Future: Share Rate% of sessions with recipe sharingTrack post-launch

    Impact Sizing Model (Assumptions made)

    • Target: 3,000 students, 25% signup ⇒ 750 users
    • Avg 3 recipes/user/week = 2,250 recipes/week
    • DAU target: 150
    • Monthly: 9,000+ recipes
    • Potential revenue: 75 premium users × $3 = $225/month

    Non-goals

    • Dietary or Allergy Filters in V1
    • Recipe History/Journaling
    • Grocery Planning and Recommendations
    • Native Mobile App
    • Elaborate Onboarding or Personalization

    Solution Alignment

    • V1 Includes: Photo upload, AI ingredient recognition, recipe generation, manual input, mobile web interface
    • V1 Excludes: Filters, planners, journaling, history, native apps

    Key Features

    • MVP: Image Upload, AI Detection, Recipe Generation, Mobile Experience
    • Post-MVP: Dietary filters, Sharing, History, Filtering, Planning, Native Apps

    Future Considerations

    • Personalization: Diet, regional cuisines
    • Community Features
    • Meal Planning
    • Notifications
    • Standalone Mobile Apps

    Key Flows

    • Image-to-Recipe: Upload → Detect → Confirm → Generate → View
    • Edge Cases: Prompt manual input for failures or ambiguities

    Key Logic

    • AI recognition with fallback
    • Recipe match by simplicity and completeness
    • Friendly error handling

    Launch Plan

    • Phase 1: Soft Rollout & Testing
    • Phase 2: Feedback Iterations
    • Phase 3: Success Monitoring & Future Readiness

    Major Risks & Contingencies

    • Recognition inaccuracy: fallback and model improvements
    • Adoption issues: direct outreach and iteration
    • Technical issues: ongoing mobile testing, API controlling

    Key Milestones

    MilestoneTimelineOwner(s)Status
    Core MVP Feature Built✅ CompleteDev TeamDone
    End-to-End QA & UX PolishWeek 1AllIn progress
    Soft Launch/Test to First UsersWeek 2PM/DevUpcoming
    Data Collection & Feedback SprintWeek 3-4PMUpcoming
    Post-MVP Roadmap DefinedWeek 4+PM/TeamUpcoming