# Semantic Caching in Enterprise RAG: Production Architectures for Faster, Lower-Cost LLM Systems

Enterprise Retrieval-Augmented Generation (RAG) systems are under increasing pressure to deliver accurate answers with lower latency and sustainable operating costs. As organizations scale from thousands to millions of daily requests, they quickly discover that the most expensive component of a RAG pipeline is rarely vector retrieval—it is repeated LLM inference for questions that have already been answered.

Imagine an enterprise support assistant receiving 50,000 queries per day.

Although every user asks questions differently, many are requesting exactly the same information.

"What is your refund policy?"

"Can I return a product?"

# Semantic Caching in Enterprise RAG: Production Architectures for Faster, Lower-Cost LLM Systems

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