Caching improves the performance and cost-efficiency of LLM-based applications by storing frequently accessed data. Standard caching saves prompts and their responses in a database but struggles with similar prompts being processed separately. Semantic caching addresses this by performing similarity searches between new and cached prompts, returning cached responses when appropriate. Implementing these caching techniques can significantly enhance the efficiency, responsiveness, and cost-effectiveness of applications.
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