Beyond Prompt Content: Optimizing LLM Performance through Format-Integrated Prompt Optimization

Large Language Models (LLMs) have made remarkable progress in recent years and demonstrated impressive capabilities in a variety of tasks. A key factor for the effective use of LLMs in practice is prompt engineering, the art of providing the right instructions and context information to the model. While research has primarily focused on optimizing prompt content, the importance of prompt formatting has often been neglected. However, the way information is structured and presented plays a crucial role in the model's performance.

A new research paper presents an innovative approach that combines the optimization of prompt content and format: Content-Format Integrated Prompt Optimization (CFPO). This method is based on an iterative refinement process where both the content and the formatting of the prompt are systematically adjusted and evaluated. For content variation, CFPO uses natural language mutations, while format exploration is controlled by a dynamic strategy that tests different formatting options.

The researchers evaluated CFPO in extensive experiments with various tasks and open-source LLMs. The results show that CFPO achieves measurable performance improvements compared to methods that exclusively optimize content. This underscores the importance of integrated content and format optimization and offers a practical, model-independent approach to increasing LLM performance. The combination of content and format optimization allows for better utilization of the strengths of LLMs and leads to more accurate and relevant results.

The Importance of Prompt Formatting

The formatting of a prompt can encompass various aspects, such as the use of delimiters, structuring through lists or tables, highlighting keywords, or using special syntax elements. A clear and structured presentation of information in the prompt can help the LLM better understand the task and extract the relevant information. For example, using tables when processing data or using bullet points when generating lists can lead to significantly better results.

The Iterative Optimization Process of CFPO

CFPO is based on an iterative process where the prompt is gradually improved. First, the prompt content is varied through natural language mutations to test different formulations and perspectives. In parallel, different formatting options are evaluated. The results of each iteration are then used to further optimize the prompt and increase the performance of the LLM. This iterative approach allows for targeted adaptation of the prompt to the specific task and the LLM being used.

Application Areas and Future Research

The results of the study demonstrate the potential of CFPO for a variety of applications, from text generation and summarization to question answering and translation. The method is model-independent and can therefore be used with various LLMs. Future research could focus on the development of even more efficient exploration strategies for format optimization as well as the application of CFPO to more complex tasks and larger LLMs. Furthermore, the development of tools and frameworks that facilitate the application of CFPO is a promising area of research.

Bibliography: Liu, Y., Xu, J., Zhang, L. L., Chen, Q., Feng, X., Chen, Y., Guo, Z., Yang, Y., & Peng, C. (2025). Beyond Prompt Content: Enhancing LLM Performance via Content-Format Integrated Prompt Optimization. arXiv preprint arXiv:2502.04295. Aclan Anthology. (2024). EMNLP Tutorials. Arxiv. (2024). Prompt Optimization: Reduce LLM Costs and Improve Performance. ResearchGate. (2024). Enhancing Large Language Model Performance through Prompt Engineering Techniques. Arxiv. (2024). Awesome LLMs ICLR 24. OpenReview. (2024). APDnmucgID. NDSS Symposium. (2024). AISCC2024-15 Paper. ScienceDirect. (2024). Enhancing Large Language Model Performance through Prompt Engineering Techniques. OpenReview. (2024). bgcdO9lmug. LinkedIn. (2024). Prompt Optimization: Reduce LLM Costs and Improve Performance.