Semantic caching is a caching strategy that stores and reuses responses based on the meaning of a query rather than its exact text. When a new query arrives, the system checks whether a semantically similar query has been answered before, and if so, returns the cached result instead of recomputing it.
This differs fundamentally from traditional caching, which requires an exact key match. A conventional cache treats how do I reset my password and what are the steps to change my password as completely different keys, missing the chance to reuse work. A semantic cache embeds the query and looks for a close match in vector space, recognising that the two questions mean the same thing and serving the same cached answer.
The benefit is reduced cost and latency, especially valuable in language model applications where generating a fresh response is slow and expensive. By short-circuiting repeated or paraphrased queries, semantic caching cuts redundant model calls and retrieval work. The main consideration is choosing the similarity threshold carefully: too loose and the cache returns answers to subtly different questions, too strict and it misses genuine matches.