In-Context Learning for E-Commerce: Redefining Dropshipping with an Automated Description Generation Framework

Abstract

The paper introduces a novel workflow leveraging in-context learning capabilities of LLMs to automate the generation of product descriptions. The proposed framework incorporates advanced techniques such as few-shot learning, chain-of-thought prompting, and selfreflection to refine the generative process. We demonstrate the efficacy of this approach using cosmetics products on the Shopee platform, achieving results that are both contextually rich and adaptable to diverse product categories. The framework is designed to be scalable and transferable, offering a generalizable solution for automated content generation across various e-commerce platforms and product types. This work represents a significant step toward redefining dropshipping and content creation in the digital marketplace through the integration of state-of-the-art artificial intelligence methods.