If you look at the query logs of any production AI application at scale whether it is a customer support bot, an internal knowledge assistant, or a coding copilot you will notice a glaring pattern. Humans are overwhelmingly predictable. User A asks: "How do I reset my password?" User B asks: "Forgot password help." User C asks: "Where is the password reset link?" If you are running a naive Generative AI architecture, you are taking all three of these prompts, passing them to a heavy LLM like Claude 3.5 Sonnet, and paying for the model to generate the exact same cognitive output three separate times. From a cloud architecture perspective, generating an LLM response is computationally expensive. If 1,000 users ask the same question in slightly different ways, you are paying for 1,000 duplicate inference cycles. To build scalable AI, we need to stop paying for identical cognitive work. We do this by placing Amazon ElastiCache (using Redis with Vector Search) in front of our LLM API to build a Semantic Cache .…