Cutting Tokens by 40% to Lower LLM API Costs Using a Memory-Efficient Algorithm

A new memory-efficient algorithm for chatbots could reduce token storage by up to 40%, significantly lowering inference costs. The approach involves having the language model respond only when users ask questions, rather than when they make statements. This selective response method allows chatbots to maintain conversational ability while minimizing unnecessary token usage and associated expenses. The innovation could make advanced AI chatbots more economically viable for developers and businesses.