This paper addresses the challenges associated with adapting Large Language Models (LLMs) for various tasks within the e-commerce domain using prompting techniques. While prompting offers an efficient alternative to fine-tuning, it often requires significant manual effort from domain experts for prompt engineering and frequent updates to align with evolving business needs. Furthermore, crafting truly unbiased natural language prompts and selecting representative in-context examples remain difficult for humans.

The authors propose a novel framework called Examples as the Prompt (EaP). This approach leverages labelled data to enhance prompts by automatically selecting the most representative examples to maximise the few-shot learning capabilities of LLMs. EaP is designed to be efficient due to its unsupervised example selection and adaptive to potential data distribution shifts.