Heuristic-based Search Algorithm in Automatic Instruction-focused Prompt Optimization: A Survey

Recent advances in Large Language Models (LLMs) have led to remarkable achievements, making prompt engineering increasingly central to guiding model outputs.While manual methods (e.g., "chain-of-thought," "step-bystep" prompts) can be effective, they typically rely on intuition and do not automatically refine prompts.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 for future opportunities for more robust and versatile LLM applications.