query handling
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Semantic Caching for AI and LLMs
Read Full Article: Semantic Caching for AI and LLMs
Semantic caching is a technique used to enhance the efficiency of AI, large language models (LLMs), and retrieval-augmented generation (RAG) systems by storing and reusing previously computed results. Unlike traditional caching, which relies on exact matching of queries, semantic caching leverages the meaning and context of queries, enabling systems to handle similar or related queries more effectively. This approach reduces computational overhead and improves response times, making it particularly valuable in environments where quick access to information is crucial. Understanding semantic caching is essential for optimizing the performance of AI systems and ensuring they can scale to meet increasing demands.
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