Semantic Caching Could Cut RAG Pipeline Costs by Handling Repeat AI Queries

A software engineer has proposed placing a semantic cache layer in front of standard RAG (Retrieval-Augmented Generation) pipelines to reduce redundant vector searches and LLM calls. The core idea targets repetitive user queries — for example, a banking chatbot where an estimated 70% of daily support questions are FAQ-type and semantically similar. Instead of matching only identical strings, a semantic cache embeds incoming questions and compares them against previously answered ones using vector similarity, returning cached responses for close matches. Under illustrative assumptions of 120,000 daily chats, answering 70% from cache at roughly 40ms rather than the 2–4 seconds of a full RAG call represents a significant latency and cost difference. The author hypothesizes that as usage grows, RAG gradually shifts from the default execution path to handling only cache misses and genuinely new questions.