- Product Description
- State of Implementation
- Use Cases
- Current Issues and Limitations
- Roadmap - Future Scope
Elevate your fashion shopping experience with the Conversational Fashion Outfit Generator – a cutting-edge addition to the Flipkart ecosystem. Seamlessly integrated into the platform, our AI-powered system redefines the way you discover, create, and personalize fashion outfits.
Powered by advanced Generative AI technology, our outfit generator engages in natural, human-like conversations to truly understand your style preferences. Leveraging your past purchase history, browsing patterns, and real-time social media trends, we deliver tailored and on-trend outfit recommendations that resonate with your unique fashion taste.
Unveil a world of possibilities as you effortlessly explore personalized outfit suggestions for every occasion. From casual outings to formal events, our generator crafts complete, well-coordinated outfits, including clothing, accessories, and footwear. With the option to interact and fine-tune outfits in a conversational manner, you're in control of your style journey.
- Natural Conversational Language Queries to Relevant Outfit Suggestion.
- User can suggest tweaks to the suggested outfit.
- Complete Outfit Generation, with a simple input.
(e.g. "I will go on a trip to the mountains. Show me everything I need.")
Personalized Outfit Recommendations via Natural Conversations:
- Enhanced User Engagement: Users interact naturally, describing their style and preferences, receiving outfit recommendations that align seamlessly with their individual tastes.
- Conversion Rate Impact: Conversational interactions lead to higher conversion rates as users find tailored products that resonate, directly contributing to revenue growth.
Precise Outfit Coordination for Enhanced Styling:
- Effortless Fashion Coordination: Users receive comprehensive outfit suggestions based on their preferences, taking into account clothing, accessories, and footwear.
- Upselling and ARPU Boost: Offering complete outfits boosts Average Revenue Per User (ARPU) and Average Order Value(AOV) as users are more likely to purchase multiple items within a well-coordinated ensemble.
Real-time Social Media Trend Integration:
- On-Trend Recommendations: By tapping into social media trends, users stay ahead of the curve with outfit suggestions aligned to the latest fashion styles.
- Conversion Rate Enhancement: Trend-sensitive recommendations drive higher conversion rates, capitalizing on users' desire for up-to-the-minute fashion choices.
Customizable Outfit Adjustments for Individual Expression:
- Tailored Personalization: Users actively engage in conversations to fine-tune outfit recommendations, ensuring the perfect look that aligns with their unique style.
- User Satisfaction and Retention: Enhanced personalization leads to higher user satisfaction, positively impacting customer retention and lifetime value.
Instant Feedback Loop for Continuous Improvement:
- User Feedback Integration: Users provide real-time feedback on recommended outfits, fostering an iterative improvement cycle.
- Conversion Rate and Loyalty: Actively involving users in the enhancement process enhances their sense of ownership, leading to increased loyalty and conversion rates.
Seamless Integration with Flipkart's Ecosystem:
- Unified Shopping Experience: The Conversational Fashion Outfit Generator seamlessly integrates into Flipkart's existing platform, providing users with a holistic shopping journey.
- Enhanced Business Metrics: Improved user engagement, conversion rates, and ARPU directly contribute to elevated profitability and key user analytics.
- Uncertain LLM Hallucination
| Issue | Description | Solution |
|---|---|---|
| Context Overload and Noise | Providing a large amount of data about the user can potentially confuse the Language Model (LLM) and lead to noisy or irrelevant responses. | Experimenting with attention mechanisms or summarization techniques to guide the LLM. |
| Lack of Information | Self Explanatory. Not Providing The LLM with enough information will lead to incorrect/misleading suggestions | Implement Filter Layers that will evaluate User Requests for Context. |
| Inadequate Domain Knowledge | LLMs may lack domain-specific knowledge about fashion, designers, and specific fashion trends, impacting the accuracy of generated recommendations. | Fine-tune LLM with specific domain knowledge and techniques. |
- User Interface
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Ideating & Decomposition of Problem Statement
- Figuring out how the solution can be integrate seamlessly into existing Flipkart Infrastructure
- Efficient Uses of Resources - Approach to leverage existing Product Recommendation Pipeline
- Defining specific usecases
- Iterate
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Data Preparation
- Collect Sample Flipkart Outfit Inventory Dataset
- Preprocess and Organize Data.
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Prototype User Interface
- Design a basic User Interface
- Enable Conversational Product Discovery
- Generate Initial Response
- Update Suggested Changes
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Prompt Designing & Engineering
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Intents Extraction & Search LLM Pipeline
- Reformat User Request
- Extract Outfit Suggestions
- Reformat Suggestions & Search Inventory
- Iterate and Improve
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Vector Database Design
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Virtual Try On
- Researched Existing Solutions and Proposals
- Implementation
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Integrate into Existing Flipkart Ecosystem
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Helpful Features
- Suggested Frequent Requests
- Enabled Voice Recognition
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