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

arXiv: https://arxiv.org/abs/2502.04295 · HTML: https://mlsanity.com/paper/2502.04295/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 66 references that are in the map.
- First posted on arXiv 6 Feb 2025.

## Builds on (most cited first, 29 of 29)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Measuring Mathematical Problem Solving With the MATH Dataset](https://mlsanity.com/paper/2103.03874/) (2021)
- [Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge](https://mlsanity.com/paper/1803.05457/) (2018)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Let's Verify Step by Step](https://mlsanity.com/paper/2305.20050/) (2023)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines](https://mlsanity.com/paper/2310.03714/) (2023)
- [Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting](https://mlsanity.com/paper/2310.11324/) (2023)
- [Automatic Prompt Optimization with "Gradient Descent" and Beam Search](https://mlsanity.com/paper/2305.03495/) (2023)
- [Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution](https://mlsanity.com/paper/2309.16797/) (2023)
- [State of What Art? A Call for Multi-Prompt LLM Evaluation](https://mlsanity.com/paper/2401.00595/) (2023)
- [Large Language Models as Tool Makers](https://mlsanity.com/paper/2305.17126/) (2023)
- [PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimization](https://mlsanity.com/paper/2310.16427/) (2023)
- [ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs](https://mlsanity.com/paper/2410.12405/) (2024)
- [Self-Taught Optimizer (STOP): Recursively Self-Improving Code Generation](https://mlsanity.com/paper/2310.02304/) (2023)
- [Does Prompt Formatting Have Any Impact on LLM Performance?](https://mlsanity.com/paper/2411.10541/) (2024)
- [Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning](https://mlsanity.com/paper/2303.02861/) (2023)
- [The Butterfly Effect of Altering Prompts: How Small Changes and Jailbreaks Affect Large Language Model Performance](https://mlsanity.com/paper/2401.03729/) (2024)
- [Mind Your Format: Towards Consistent Evaluation of In-Context Learning Improvements](https://mlsanity.com/paper/2401.06766/) (2024)
- [Language Models Can Teach Themselves to Program Better](https://mlsanity.com/paper/2207.14502/) (2022)
- [POSIX: A Prompt Sensitivity Index For Large Language Models](https://mlsanity.com/paper/2410.02185/) (2024)
- [MAGIC: Generating Self-Correction Guideline for In-Context Text-to-SQL](https://mlsanity.com/paper/2406.12692/) (2024)
- [Symbolic Prompt Program Search: A Structure-Aware Approach to Efficient Compile-Time Prompt Optimization](https://mlsanity.com/paper/2404.02319/) (2024)
- [LangGPT: Rethinking Structured Reusable Prompt Design Framework for LLMs from the Programming Language](https://mlsanity.com/paper/2402.16929/) (2024)
- [Q-Tuning: Queue-based Prompt Tuning for Lifelong Few-shot Language Learning](https://mlsanity.com/paper/2404.14607/) (2024)

## Built on by (most cited first, 16 of 16)

- [SkCC: Portable and Secure Skill Compilation for Cross-Framework LLM Agents](https://mlsanity.com/paper/2605.03353/) (2026)
- [Benchmarking Chinese Medical LLMs: A Medbench-based Analysis of Performance Gaps and Hierarchical Optimization Strategies](https://mlsanity.com/paper/2503.07306/) (2025)
- [Bounds of Chain-of-Thought Robustness: Reasoning Steps, Embed Norms, and Beyond](https://mlsanity.com/paper/2509.21284/) (2025)
- [Prompt and Parameter Co-Optimization for Large Language Models](https://mlsanity.com/paper/2509.24245/) (2025)
- [A Survey of LLM Prompt Datasets: Taxonomy, Linguistic Patterns, and Practical Uses](https://mlsanity.com/paper/2510.09316/) (2025)
- [Knowing How to Edit: Reliable Evaluation Signals for Diagnosing and Optimizing Prompts at Query Level](https://mlsanity.com/paper/2511.19829/) (2025)
- [View-oriented Conversation Compiler for Agent Trace Analysis](https://mlsanity.com/paper/2603.29678/) (2026)
- [Prompt Orchestration Markup Language](https://mlsanity.com/paper/2508.13948/) (2025)
- [FMBench: Adaptive Large Language Model Output Formatting](https://mlsanity.com/paper/2602.06384/) (2026)
- [How Confident Is the First Token? An Uncertainty-Calibrated Prompt Optimization Framework for Large Language Model Classification and Understanding](https://mlsanity.com/paper/2603.18009/) (2026)
- [Optimization before Evaluation: Evaluation with Unoptimised Prompts Can be Misleading](https://mlsanity.com/paper/2604.27637/) (2026)
- [Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap](https://mlsanity.com/paper/2505.19625/) (2025)
- [Reasoning with Preference Constraints: A Benchmark for Language Models in Many-to-One Matching Markets](https://mlsanity.com/paper/2509.13131/) (2025)
- [OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms](https://mlsanity.com/paper/2604.26211/) (2026)
- [GTA: Graph Theory Agent and Benchmark for Algorithmic Graph Reasoning with LLMs](https://mlsanity.com/paper/2609.12265/) (2026)
- [MoPrO-SD: Modular Prompt Optimization for Stance Detection](https://mlsanity.com/paper/2610.13386/) (2026)

## Abstract (arXiv)

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.