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The article highlights a significant issue with large language models (LLMs): despite 70% of user queries being semantically identical, traditional caching methods fail to recognize this redundancy, leading to skyrocketing costs. Poorly implemented semantic caching can even be exploited by malicious actors to corrupt the model's knowledge base, resulting in harmful or incorrect responses for genuine users. This problem is especially critical in e-commerce customer support chatbots, where understanding and efficiency are paramount. Addressing these inefficiencies and security vulnerabilities could revolutionize how we manage LLMs in practical applications.