Large language models(LLM) such as ChatGPT have substantially simplified the generation of marketing copy, yet producing content satisfying domain specific requirements, such as effectively engaging customers, remains a significant challenge. In this work, we introduce the Genetic Copy Optimization Framework (GCOF) designed to enhance both efficiency and engagememnt of marketing copy creation. We conduct explicit feature engineering within the prompts of LLM. Additionally, we modify the crossover operator in Genetic Algorithm (GA), integrating it into the GCOF to enable automatic feature engineering. This integration facilitates a self-iterative refinement of the marketing copy. Compared to human curated copy, Online results indicate that copy produced by our framework achieves an average increase in click-through rate (CTR) of over $50\%$.

翻译：诸如ChatGPT等大型语言模型（LLM）已显著简化了营销文案的生成过程，但如何产出满足领域特定需求（例如有效吸引客户）的内容仍是一大挑战。本文提出遗传优化文案框架（GCOF），旨在提升营销文案创作的效率与客户参与度。我们在LLM提示词中显式进行特征工程，并改进遗传算法（GA）中的交叉算子，将其集成至GCOF以实现自动特征工程。该集成机制促进了营销文案的自迭代优化。与人工撰写的文案相比，在线实验结果表明，我们的框架生成的文案点击率（CTR）平均提升超过50%。