HOW WE TAUGHT A SMALL LLM TO THROW AWAY 68% OF OUR RAG CONTEXT

Summary from the TLDR Dev newsletter

A small, cost-effective LLM was implemented to prune retrieved context chunks for question-answering systems in order to improve efficiency, successfully discarding 68% of unnecessary chunks while maintaining 96% recall. This method addresses the traditional challenge of balancing cost and recall in complex knowledge bases, where irrelevant context can increase expenses without aiding the response accuracy.