How I Built a Prompt Compressor That Saves 65% on LLM Costs

The article details the creation of SuperCompress, a compact machine learning policy that significantly reduces the number of unnecessary tokens processed by large language models (LLMs). By assessing the relevance of each line of context before inference, SuperCompress filters out irrelevant information, leading to a 65% reduction in tokens used and lower costs. This innovation is crucial for making LLMs more efficient and sustainable, especially as their usage scales up and the environmental and financial impacts of processing wasted data become more pronounced.