Documentation Index
Fetch the complete documentation index at: https://www.adaline.ai/docs/llms.txt
Use this file to discover all available pages before exploring further.
System
User
User Prompt
LLM Response
You are an expert Product Manager specializing in creating comprehensive product specifications. Generate a well-structured product specification document that includes:
- Executive Summary with clear objectives
- Target User Analysis with personas
- Detailed Feature Specifications with user stories
- Technical Requirements and Success Metrics
- Implementation Timeline and Risk Assessment
Format with clear headings and actionable details ready for team review. Keep it comprehensive yet concise. Create a product specification for my {{product_type}}:
- Product Name:
{{product_name}}
- Key Features:
{{feature_list}}
- Target Users:
{{target_users}}
- Success Metrics:
{{success_metrics}}
Create a product specification for my mobile app:
- Product Name: FoodSnap - AI Recipe Generator
- Key Features: Photo-based ingredient recognition, personalized recipe suggestions, dietary restriction filters, shopping list generation
- Target Users: Busy professionals aged 25-40 who cook at home but lack inspiration and time for meal planning
- Success Metrics: 80% user retention after 30 days, average 3 recipes generated per user per week, 4.5+ app store rating
Product Specification: FoodSnap - AI Recipe GeneratorExecutive Summary
FoodSnap is an AI-powered mobile app that transforms ingredient photos into personalized recipe recommendations, targeting busy professionals who want quick, tailored cooking solutions.Target User Analysis
Primary Persona: Busy Professional Cook (Age 25-40)
- Goals: Quick meal ideas, use existing ingredients, healthy eating
- Pain Points: Lack of cooking inspiration, food waste, time constraints
Feature Specifications
-
AI Ingredient Recognition
- User Story: “As a user, I want to snap a photo of my fridge contents and get recipe suggestions”
- Requirements: Computer vision API, ingredient database, accuracy >90%
-
Personalized Recipe Engine
- User Story: “As a user, I want recipes that match my dietary preferences and skill level”
- Requirements: User preference profiling, recipe difficulty scoring, dietary filters
-
Smart Shopping Lists
- User Story: “As a user, I want to generate shopping lists for missing ingredients”
- Requirements: Recipe parsing, inventory tracking, grocery store integration
Technical Requirements
- Platform: iOS/Android native apps
- Backend: Cloud-based ML models, recipe database API
- Performance:
<3 second photo processing, offline recipe viewing
Success Metrics
- User Retention: 80% after 30 days
- Engagement: 3+ recipes generated per user weekly
- Satisfaction: 4.5+ app store rating
- Conversion: 25% of users upgrade to premium within 60 days
Implementation Timeline
- Phase 1 (Months 1-2): Core photo recognition, basic recipe database
- Phase 2 (Months 3-4): Personalization engine, dietary filters
- Phase 3 (Months 5-6): Shopping list integration, premium features
Risk Assessment
- Technical: AI accuracy may vary with photo quality
- Mitigation: Extensive training data, user feedback loop
- Market: Competitive recipe app landscape
- Mitigation: Focus on unique photo-to-recipe workflow
