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Implement a Semantic Cache for LLM Responses is a medium coding interview question covering vector search, design, machine learning — the kind asked at FAANG (Facebook/Meta, Apple, Amazon, Netflix, Google) and AI-first companies like Anthropic, OpenAI, and Nvidia.
LLM calls are slow and expensive, and users endlessly paraphrase the same questions - but exact-string caching never hits. Implement a semantic cache that stores (embedding, response) pairs and serves a cached response when a new query's embedding is within a cosine-similarity threshold of a cached one.
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