Cache Retrieval

Cache Retrieval is an optimization technique that stores frequently accessed data or computation results in high-speed memory systems to reduce latency and computational overhead in AI applications. This process involves maintaining cached embeddings, query results, model outputs, or intermediate computations that can be quickly retrieved for similar or identical requests. Cache retrieval systems implement various strategies including Least Recently Used (LRU), Most Recently Used (MRU), and semantic similarity-based caching for vector databases and retrieval systems. Key components include cache hit/miss detection, cache invalidation policies, and cache warming strategies. In machine learning contexts, cache retrieval accelerates inference by storing pre-computed embeddings, attention weights, or feature representations. Modern AI systems employ multi-level caching architectures combining memory caches, disk-based storage, and distributed caching layers. Cache retrieval is essential for production AI systems requiring low-latency responses, cost optimization, and efficient resource utilization in applications like real-time recommendation engines and conversational AI platforms.