Originally published on AI Tech Connect.
What semantic caching actually is Most LLM applications answer the same questions over and over, phrased slightly differently each time. A support assistant fields "how do I reset my password", "I forgot my password" and "can't log in, need a new password" as three distinct requests, and pays for three full inferences to produce three near-identical answers. Semantic caching removes that waste. It embeds each incoming query as a vector, cosine-matches that vector against the vectors of queries it has already answered, and — if the closest stored query is similar enough — serves the response it saved last time, without calling the model at all. The loop is short: embed the query, search the vector store for the nearest neighbour, compare the similarity score against a threshold, and either…
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