What is Semantic Caching

Definition

Semantic caching stores the responses to previous AI queries indexed by the meaning of the query, so that a new request similar in intent can be answered from the cache rather than re-invoking the model, reducing cost and latency for repeated or near-duplicate questions.

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Cuts cost and latency by reusing answers to semantically similar queries
Matches by meaning, so paraphrased questions still hit the cache
Reduces load on expensive model inference for common questions
Improves response speed for users asking familiar things

Real World Example

A support assistant uses semantic caching so that 'how do I reset my password' and 'I forgot my password, what now' both retrieve the same cached, vetted answer instead of calling the LLM twice.

FAQs

How does semantic caching differ from normal caching?

Normal caching requires an exact key match, while semantic caching matches by similarity of meaning, catching paraphrased queries.

What are the benefits of semantic caching?

Lower inference cost, reduced latency, and less load on the model for repeated or similar questions.

What is a risk of semantic caching?

Overly loose similarity matching could return a cached answer that does not quite fit, so thresholds must be tuned carefully.