Semantic caching can reduce repeated model calls by matching a new query to a stored request with similar meaning, even when the wording differs. It can be useful for common questions, support assistants and retrieval pipelines where many users ask near-duplicate questions.
A help center bot might see "how do I reset my password" and "password reset steps" many times a day; a semantic cache can answer the second from the first stored response. Set expiry times so outdated answers are removed, and skip caching for requests that depend on personal data or live information such as order status. Matching usually relies on embeddings.
Similarity thresholds require careful testing: a false match can return an answer that is irrelevant or unsafe for the current context. Include user permissions and freshness in cache keys where needed, and avoid sharing private responses across users. Related: Semantic Search, LLM Inference.