Retail-GPT: leveraging Retrieval Augmented Generation (RAG) for building E-commerce Chat Assistants
On the map: Retrieval-augmented generation → RAG pipeline design and evaluation
Abstract (arXiv)
This work presents Retail-GPT, an open-source RAG-based chatbot designed to enhance user engagement in retail e-commerce by guiding users through product recommendations and assisting with cart operations. The system is cross-platform and adaptable to various e-commerce domains, avoiding reliance on specific chat applications or commercial activities. Retail-GPT engages in human-like conversations, interprets user demands, checks product availability, and manages cart operations, aiming to serve as a virtual sales agent and test the viability of such assistants across different retail businesses.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. First posted on arXiv 15 Aug 2024.
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