Semantic Caching

What is semantic caching?

Classic caching is all about exact matches: two requests have to look identical before you return the last result. Semantic caching is smarter — it understands meaning. Even if a user rephrases the question, if the intent is the same, the cache recognizes "I've answered this one" and reuses the old answer, skipping a fresh computation.

How is it different from normal caching?

Normal caching: looks at the letters
"How do I change my password" and "How do I update my password" count as two different requests, so it computes twice for nothing.
Semantic caching: looks at the meaning
It turns the question into a semantic vector and compares similarity, not characters. Close meaning, cache hit.

How does it work?

Turn the question into a vector
An embedding model encodes the query into a high-dimensional vector; similar meanings land close together.
Compare similarity
When a new question arrives, measure the distance to cached vectors. Past a threshold, it's "the same kind of question".
Hit means return instantly
With a good hit rate, plenty of requests never touch the big model at all — cost and latency both drop.

Where does it shine?

Support Q&A, FAQs and high-repeat SaaS products benefit most — users keep asking the same handful of things in different words. Just watch the threshold: set it too loose and you'll treat "similar but different" questions as the same, which is how you serve wrong answers.

Bottom line: semantic caching helps AI recognize "same question, different words" and reuse old answers — cheaper and faster.