# A Survey of Automatic Prompt Engineering: An Optimization Perspective

arXiv: https://arxiv.org/abs/2502.11560 · HTML: https://mlsanity.com/paper/2502.11560/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2025.

## Built on by (most cited first, 30 of 41)

- [Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities](https://mlsanity.com/paper/2507.13158/) (2025)
- [Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for MLLMs](https://mlsanity.com/paper/2510.09201/) (2025)
- [HiveMind: Contribution-Guided Online Prompt Optimization of LLM Multi-Agent Systems](https://mlsanity.com/paper/2512.06432/) (2025)
- [Revisiting Prompt Optimization with Large Reasoning Models-A Case Study on Event Extraction](https://mlsanity.com/paper/2504.07357/) (2025)
- [Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study](https://mlsanity.com/paper/2506.19773/) (2025)
- [promptolution: A Unified, Modular Framework for Prompt Optimization](https://mlsanity.com/paper/2512.02840/) (2025)
- [Causal Prompting for Implicit Sentiment Analysis with Large Language Models](https://mlsanity.com/paper/2507.00389/) (2025)
- [Transduction is All You Need for Structured Data Workflows](https://mlsanity.com/paper/2508.15610/) (2025)
- [Structured Prompts Improve Evaluation of Language Models](https://mlsanity.com/paper/2511.20836/) (2025)
- [JudgeFlow: Agentic Workflow Optimization via Block Judge](https://mlsanity.com/paper/2601.07477/) (2026)
- [LLM-based Question-Answer Framework for Sensor-driven HVAC System Interaction](https://mlsanity.com/paper/2507.04748/) (2025)
- [Stabilizing Black-Box Prompt Optimization with Textual Regularization and Signal Aggregation](https://mlsanity.com/paper/2507.09839/) (2025)
- [APIO: Automatic Prompt Induction and Optimization for Grammatical Error Correction and Text Simplification](https://mlsanity.com/paper/2508.09378/) (2025)
- [Are My Optimized Prompts Compromised? Exploring Vulnerabilities of LLM-based Optimizers](https://mlsanity.com/paper/2510.14381/) (2025)
- [Towards Adaptive, Scalable, and Robust Coordination of LLM Agents: A Dynamic Ad-Hoc Networking Perspective](https://mlsanity.com/paper/2602.08009/) (2026)
- [LPDS: Evaluating LLM Robustness Through Logic-Preserving Difficulty Scaling](https://mlsanity.com/paper/2605.15393/) (2026)
- [LatentPrompt: Optimizing Promts in Latent Space](https://mlsanity.com/paper/2508.02452/) (2025)
- [UniAPO: Unified Multimodal Automated Prompt Optimization](https://mlsanity.com/paper/2508.17890/) (2025)
- [Instance-level Randomization: Toward More Stable LLM Evaluations](https://mlsanity.com/paper/2509.12678/) (2025)
- [Reasoning-Aware Prompt Orchestration: A Foundation Model for Multi-Agent Language Model Coordination](https://mlsanity.com/paper/2510.00326/) (2025)
- [Mathematics with large language models as provers and verifiers](https://mlsanity.com/paper/2510.12829/) (2025)
- [MA-SAPO: Multi-Agent Reasoning for Score-Aware Prompt Optimization](https://mlsanity.com/paper/2510.16635/) (2025)
- [POaaS: Minimal-Edit Prompt Optimization as a Service to Lift Accuracy and Cut Hallucinations on On-Device sLLMs](https://mlsanity.com/paper/2603.16045/) (2026)
- [Context Engineering: A Practitioner Methodology for Structured Human-AI Collaboration](https://mlsanity.com/paper/2604.04258/) (2026)
- [MO-CAPO: Multi-Objective Cost-Aware Prompt Optimization](https://mlsanity.com/paper/2605.18869/) (2026)
- [From Demonstrations to Rewards: Test-Time Prompt Optimization for VLM Reward Models](https://mlsanity.com/paper/2606.00083/) (2026)
- [Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap](https://mlsanity.com/paper/2505.19625/) (2025)
- [Parameter-Efficient Adaptation of mPLUG-Owl2 via Pixel-Level Visual Prompts for NR-IQA](https://mlsanity.com/paper/2509.03494/) (2025)
- [Prompt-Based Value Steering of Large Language Models](https://mlsanity.com/paper/2511.16688/) (2025)
- [2K-Characters-10K-Stories: A Quality-Gated Stylized Narrative Dataset with Disentangled Control and Sequence Consistency](https://mlsanity.com/paper/2512.05557/) (2025)

## Abstract (arXiv)

The rise of foundation models has shifted focus from resource-intensive fine-tuning to prompt engineering, a paradigm that steers model behavior through input design rather than weight updates. While manual prompt engineering faces limitations in scalability, adaptability, and cross-modal alignment, automated methods, spanning foundation model (FM) based optimization, evolutionary methods, gradient-based optimization, and reinforcement learning, offer promising solutions. Existing surveys, however, remain fragmented across modalities and methodologies. This paper presents the first comprehensive survey on automated prompt engineering through a unified optimization-theoretic lens. We formalize prompt optimization as a maximization problem over discrete, continuous, and hybrid prompt spaces, systematically organizing methods by their optimization variables (instructions, soft prompts, exemplars), task-specific objectives, and computational frameworks. By bridging theoretical formulation with practical implementations across text, vision, and multimodal domains, this survey establishes a foundational framework for both researchers and practitioners, while highlighting underexplored frontiers in constrained optimization and agent-oriented prompt design.