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Design a semantic cache for LLM responses that cuts cost and latency without serving stale or wrong answers.

Exact-match caching barely helps when no two prompts are identical. Semantic caching reuses answers for similar queries, and its whole risk is returning a near-match that's subtly wrong. Here is how to build it safely.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

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Applied AI Engineering·ProductionSign in13mCaching, and the one that pays for itself immediatelyThree kinds of caching apply here and they are not interchangeable. One is nearly free and often sits unused, one needs care, and one will serve wrong answers if you are careless about what belongs in the key.

UP NEXT ON YOUR JOURNEY

Next in this trackDesign a multi-region, highly available LLM serving platform with failover and bounded cost.Next in this trackDesign a system to run LLM inference over a billion documents offline, as cheaply as possible.Next in this trackDesign an internal evaluation platform that lets teams measure and compare LLM features reliably.

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