Prompt compression stands as a pivotal strategy in optimizing the interaction with Large Language Models (LLMs), fundamentally addressing the inherent challenges of context window limitations, escalating operational costs, and increasing inference latency. At its core, prompt compression involves intelligently reducing the token count of the input provided to an LLM while preserving, or even enhancing, the essential information and intent. This sophisticated approach moves beyond mere brevity, delving
