Beyond Prompt Content: Enhancing LLM Performance via Content-Format Integrated Prompt Optimization

TL;DR

This paper introduces CFPO, a method that jointly optimizes prompt content and formatting to improve large language model performance, demonstrating significant gains over content-only approaches across various tasks and models.

Contribution

The paper presents a novel, systematic approach to optimize both prompt content and format simultaneously, filling a gap in prompt engineering research.

Findings

CFPO outperforms content-only optimization methods in multiple tasks.

Content-format integrated optimization yields measurable performance improvements.

The approach is model-agnostic and practical for real-world applications.

Abstract

Large Language Models (LLMs) have shown significant capability across various tasks, with their real-world effectiveness often driven by prompt design. While recent research has focused on optimizing prompt content, the role of prompt formatting, a critical but often overlooked dimension, has received limited systematic investigation. In this paper, we introduce Content-Format Integrated Prompt Optimization (CFPO), an innovative methodology that jointly optimizes both prompt content and formatting through an iterative refinement process. CFPO leverages natural language mutations to explore content variations and employs a dynamic format exploration strategy that systematically evaluates diverse format options. Our extensive evaluations across multiple tasks and open-source LLMs demonstrate that CFPO demonstrates measurable performance improvements compared to content-only optimization methods. This highlights the importance of integrated content-format optimization and offers a practical, model-agnostic approach to enhancing LLM performance. Code is available at https://github.com/HenryLau7/CFPO.