RAG-Powered AI Shopping Assistant

Overview: This project explores how cutting-edge AI technologies, specifically Retrieval-Augmented Generation (RAG) and Azure Prompt Flow, can transform the eCommerce experience. By leveraging advanced language models (LLMs) such as OpenAI's GPT or Gemini, this solution demonstrates a smarter, more personalized approach to customer interactions in online retail.

Smart Product Discovery: The AI assistant helps customers find the exact products they need based on specific queries. By analyzing a publicly available Flipkart clothing dataset, the assistant can recommend items, ensuring a seamless shopping experience.

Instant Order Updates: Customers can inquire about their order status and receive real-time updates, eliminating the frustration of waiting for customer service responses.

Policy-Based Query Handling: The system is designed to interpret and respond to customer queries—like returns or shipping details—based on the company's specific policies, making it highly adaptable to individual eCommerce stores.

RAG Framework: Combines document retrieval with generative AI to enhance response accuracy. This allows the assistant to access relevant product data and policies in real time for tailored answers.

Azure Prompt Flow: Enables the creation of dynamic workflows and custom prompts, allowing the integration of various LLMs like OpenAI, Gemini, and others to deliver precise and context-aware outputs.

Large Language Models (LLMs): Leverages state-of-the-art LLMs to generate natural, conversational responses, enhancing user experience with a human-like touch.

Prototype Results: Though in its early stages, the prototype demonstrated:

Enhanced Customer Engagement: Customers interacted more smoothly with the AI assistant than with traditional chatbots.

Improved Query Resolution: By using RAG, the system provided accurate answers based on product and policy data.

Scalability Potential: The modular design allows it to scale across various industries and datasets.

Challenges and Next Steps: While the current UI is basic, the project underscores the potential for a polished, scalable solution. The next phase will focus on refining the user interface, expanding datasets, and testing with live eCommerce environments to validate its effectiveness in real-world scenarios.

Conclusion: The RAG-Powered AI Shopping Assistant isn’t just about technology—it’s about reimagining the way customers shop online. By combining advanced AI capabilities with a human-like touch, this project points to a future where eCommerce is not only smarter but also more personal and satisfying.