RAG-Powered AI Shopping Assistant

Muhammad Hassan

0

AI Agent Developer

Web Developer

AI Developer

Azure

Flask

OpenAI

Demo of product retrieval
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.
Key Features:
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.
Technology Stack:
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.
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Posted Jan 3, 2025

AI-powered shopping assistant using RAG, Azure Prompt Flow, and LLMs to transform eCommerce with smart, personalized, and scalable solutions.

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AI Agent Developer

Web Developer

AI Developer

Azure

Flask

OpenAI

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