The escalating operational costs of Large Language Models (LLMs) present a significant challenge for businesses and developers alike, largely driven by the token-based pricing models of prominent providers. Input tokens, representing the prompt sent to the LLM, often constitute the majority of these expenses, alongside influencing latency and the constraints of context window limits. Smart prompt compression strategies are not merely an optimization; they are an economic imperative and
