The burgeoning field of large language models (LLMs) has revolutionized how humans interact with artificial intelligence, enabling sophisticated text generation, summarization, and question-answering. However, harnessing their full potential often involves crafting intricate prompts, which can quickly become verbose and exceed practical limits. Prompt compression emerges as a critical technique designed to distill these lengthy instructions into a concise, token-efficient format without sacrificing essential
