Reimagining the Ecommerce Customer Experience with LLMs, Vector Search, and Retrieval-Augmented Generation

The blog post discusses how large language models (LLMs), vector search, and retrieval-augmented generation (RAG) are revolutionizing product recommendations in ecommerce. LLMs enable understanding and generating human-like text by processing textual descriptions, features, and attributes of products to create embeddings. Vector search enhances the shopping experience by providing highly personalized recommendations with greater accuracy, relevance, and context. RAG combines retrieval-based and generative approaches, where vector search locates candidate products aligned with a query's semantics, and LLMs craft human-like text descriptions or recommendations for each product. The synergy of these technologies offers a glimpse into the future of online shopping, where virtual assistants understand customer desires and provide contextually relevant product recommendations.