In our era of self-assembly and DIY projects, the challenge of understanding and following diverse instructional guides is a common hurdle. From assembling a piece of furniture to building a complex model airplane, the range of tasks calling for clear, step-by-step guidance is vast. This creates a unique opportunity for a technological solution that simplifies and enhances this process.
Problem Statement
The task is to create Diyn a versatile Conversational Al designed to assist people with cooking and finding recipes. The goal is to provide an interactive, Al-driven assistant that makes the cooking process easier, faster, and more efficient at home, or at a restaurant setup while onboarding a new chef into the team.
Data
The data needs to come from the database of the platform, the format of the data could be any possibility that can allow creation and maintenance of recipes.
The user is free to choose any available recipe on the platform and use the interface for reading, marking and questioning.
Technical Challenges
Robust Assistant Development: Al based assistant capable of interpreting various cooking recipes. This includes sophisticated natural language processing and computer vision algorithms to understand user questions and provide accurate, helpful responses.
Accurate Responses: The assistant should be able to accurately answer queries, summarise sections, and retrieve information from the recipes on the platform.
Adaptive Learning and Personalisation: Ensure the Conversational Al can adapt to different user comprehension levels and learning styles, offering personalised assistance based on user interactions and feedback.
User-Friendly Interface Design: Design an intuitive and engaging interface that facilitates easy interaction with Conversational Al across various devices and platforms that will enable users to find different recipes.
[x] Target User
Home Cooks: Beginners, intermediate, adventurous. Offer basic guidance, substitutions, and variations.
New Chefs: Focus on efficiency, technique explanations, and handling unfamiliar ingredients.
[x] Scope of Diyn's Capabilities
Answer recipe-related questions (substitutions, timings, techniques, conversions).
Offer personalised recipe recommendations based on preferences and dietary restrictions.
Provide basic cooking advice.
[x] Data Integration and Security
Secure API access to recipe database, encrypt user data.
Address privacy concerns with clear data usage policies and opt-in options.
[x] Performance Metrics
Track accuracy of answers and recipe summaries.
Monitor user engagement metrics (usage time, frequency, feedback).
Measure Diyn's adaptation to different user interaction styles.
[x] Ethical Considerations
Implement bias detection and mitigation algorithms in recipe recommendations.
Fact-check information and provide sources for recipe claims.
Develop clear guidelines for user interaction and potential misuse prevention.
[x] Scalability and Future Development
Explore expansion to other domains like meal planning and grocery shopping.
Consider integration with smart kitchen appliances and voice assistants.
Plan for ongoing updates and maintenance to ensure Diyn stays relevant and accurate.
shankarlohar
commented
9 months ago
Define
[x] Target User
[x] Scope of Diyn's Capabilities
[x] Data Integration and Security
[x] Performance Metrics
[x] Ethical Considerations
[x] Scalability and Future Development