A Survey of Automatic Prompt Optimization with Instruction-focused Heuristic-based Search Algorithm
This work provides a structured overview for researchers and practitioners in NLP to advance automated prompt refinement, but it is incremental as it surveys existing methods rather than introducing new ones.
This survey tackles the problem of manual prompt engineering being inefficient by proposing a taxonomy for automatic prompt optimization methods using heuristic-based search algorithms, categorizing them based on optimization aspects and highlighting supporting tools.
Recent advances in Large Language Models have led to remarkable achievements across a variety of Natural Language Processing tasks, making prompt engineering increasingly central to guiding model outputs. While manual methods can be effective, they typically rely on intuition and do not automatically refine prompts over time. In contrast, automatic prompt optimization employing heuristic-based search algorithms can systematically explore and improve prompts with minimal human oversight. This survey proposes a comprehensive taxonomy of these methods, categorizing them by where optimization occurs, what is optimized, what criteria drive the optimization, which operators generate new prompts, and which iterative search algorithms are applied. We further highlight specialized datasets and tools that support and accelerate automated prompt refinement. We conclude by discussing key open challenges pointing toward future opportunities for more robust and versatile LLM applications.