Learn by Selling: Equipping Large Language Models with Product Knowledge for Context-Driven Recommendations

In contrast, LLMs, with their ability to understand nuances of language and context, offer a promising solution The rapid evolution of large language models (LLMs) has opened to overcome these limitations. However, a crucial prerequisite up new possibilities for applications such as context-driven product for LLMs to excel in product recommendation is their possession of recommendations. However, the effectiveness of these models in comprehensive knowledge about the entire inventory of products this context is heavily reliant on their comprehensive understanding available for sale. of the product inventory. This paper presents a novel approach In this paper, we propose a novel approach to equip LLMs with to equipping LLMs with product knowledge by training them to respond product knowledge by training them to generate contextual responses contextually to synthetic search queries that include product to synthetic search queries containing product IDs.