Cut an Enterprise RAG Pipeline’s Latency and Cost by Calling the LLM Less, Not by Buying a Faster Model

Enterprise RAG pipelines often incur unnecessary latency and cost by calling an LLM for every question, even simple ones. A more efficient approach is to implement a router that assesses the initial retrieval results before engaging the model. This router uses a confidence signal, specifically the score of the top keyword match and the margin between it and the runner-up, to decide the path. If the signal is strong, the question bypasses the expensive LLM calls and uses a faster, deterministic extractor, saving significant time and resources on straightforward queries.