How to Reduce Cost and Latency of Your RAG Application Using Semantic LLM Caching

Semantic caching in Large Language Model (LLM) applications enhances efficiency by leveraging embeddings and similarity search to reuse responses based on semantic content rather than exact text matches. When a user submits a query, it is transformed into a vector embedding, which is then compared against cached embeddings using Approximate Nearest Neighbor (ANN) algorithms; if a close match exceeds a predefined similarity threshold, the cached response is returned instantly, bypassing the resource-intensive generation process. This approach significantly reduces both latency and API costs, especially for repeated or paraphrased questions, by storing only relevant query-response pairs in the cache within