caching in retrieval

**Caching in retrieval** is the **performance optimization that stores reusable retrieval artifacts to reduce repeated compute and index access** - caching lowers latency and infrastructure load when query patterns repeat. **What Is Caching in retrieval?** - **Definition**: Temporary storage of retrieval outputs or intermediate computations. - **Cache Targets**: May include result lists, embeddings, filter plans, and reranker features. - **Policy Dimensions**: Uses eviction, TTL, and invalidation rules tied to data freshness needs. - **Pipeline Position**: Applied at API edge, retriever service, and vector lookup layers. **Why Caching in retrieval Matters** - **Latency Reduction**: Cache hits bypass expensive retrieval steps and return faster responses. - **Cost Savings**: Repeated compute and vector operations are reduced significantly. - **Burst Handling**: Caches smooth traffic spikes for popular or repetitive queries. - **System Stability**: Lower backend load reduces timeout and overload risk. - **User Consistency**: Frequent queries receive predictable response times. **How It Is Used in Practice** - **Key Design**: Build canonical cache keys from normalized query plus filter context. - **Freshness Strategy**: Use TTLs and event-driven invalidation when source data changes. - **Hit Monitoring**: Track hit rate, staleness incidents, and eviction churn for tuning. Caching in retrieval is **a primary performance lever in high-traffic retrieval services** - effective cache design improves speed and cost without sacrificing evidence quality.