Semantic Caching in RAG Systems Cuts Redundant LLM and Vector DB Calls

Semantic caching is a technique used in Retrieval-Augmented Generation (RAG) pipelines to avoid redundant lookups when users submit identical or similar queries. Instead of querying the vector database and large language model each time, the system stores previous query-answer pairs in a fast in-memory store such as Redis or Valkey. Because these stores traditionally match keys exactly, embeddings and cosine similarity are used to identify semantically equivalent queries even when the wording differs. Cache invalidation is a critical consideration, as time-sensitive answers — like commodity prices — must expire before they become outdated. Since RAM capacity is limited, only high-value, frequently repeated queries should be cached to keep the strategy both effective and resource-efficient.