While prompt caching in LLMs saves costs, developers can extend caching across the entire RAG pipeline. Beyond storing document embeddings, three additional layers offer substantial efficiency gains. These include caching query embeddings, retrieval results, and full responses, each addressing different reuse scenarios and staleness requirements.

Implementing these requires external stores like Redis for exact-match key-value pairs or ChromaDB for semantic similarity searches. A query-embedding cache avoids recomputing vectors for repeated queries. A separate retrieval cache stores document chunks, which may need refreshing more frequently than embeddings if the knowledge base updates, necessitating different TTL policies.

The core insight is that not all cacheable elements share the same volatility. Query embeddings and retrieved chunks serve distinct purposes with independent lifecycles. By strategically applying exact-match and semantic caching at multiple stages, teams can minimize redundant model calls and vector searches, directly reducing both latency and operational costs for high-traffic AI applications.