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

A production RAG pipeline that runs three LLM calls in series for every question can skip most of that cost on easy, templated questions by reading a signal the retrieval step already computes: the top keyword score and its margin over the runner-up. When one line scores far above the rest, a deterministic extractor returns the answer with no model call, saving roughly two seconds of latency and three billed calls per question; when scores are flat, the full pipeline (arbiter plus generation) runs as before. The routing decision itself runs in about 0.1 millisecond. The piece situates this router alongside other deterministic indicators (cached answers, dictionary-based question typing) and distinguishes it from agentic RAG, where the model itself decides the route rather than a fixed rule.

Source: https://towardsdatascience.com/cut-an-enterprise-rag-pipelines-latency-and-cost-by-calling-the-llm-less-not-by-buying-a-faster-model. 8 Sync News only summarizes and links out; content copyright belongs to the authors and original sources.