retrieval latency

**Retrieval latency** is the **end-to-end time required for the retrieval layer to return candidates for a query** - latency budgets shape user experience and determine whether RAG systems can support interactive workloads. **What Is Retrieval latency?** - **Definition**: Measured delay from retrieval request receipt to ranked candidate output. - **Latency Components**: Includes network overhead, index lookup, score computation, and rerank setup. - **Measurement Scope**: Tracked as p50, p95, and p99 to capture tail behavior. - **Pipeline Coupling**: Directly impacts total answer time in retrieval-augmented generation. **Why Retrieval latency Matters** - **User Experience**: Slow retrieval creates visible lag even with fast generation models. - **SLA Compliance**: Production systems must hit strict response-time objectives. - **Throughput Interaction**: Latency spikes often indicate contention that also lowers capacity. - **Cost Pressure**: Expensive reranking and oversize top-k values can inflate response time. - **Reliability Signal**: Tail latency degradation is an early warning for infrastructure stress. **How It Is Used in Practice** - **Budget Decomposition**: Assign per-stage latency budgets across retrieval, reranking, and generation. - **Index Optimization**: Tune ANN parameters, caching, and data locality for faster candidate fetch. - **Observability**: Instrument distributed tracing to isolate bottlenecks at query and shard level. Retrieval latency is **a first-class performance metric in RAG operations** - tight latency control is required for responsive and scalable AI search experiences.