Reranking Optimisation — Reduce Latency, Maintain Precision

I will optimise your reranking pipeline for production latency — implementing model quantisation to reduce inference time, adaptive reranking depth (reranking fewer documents for simple queries, more for complex ones), result caching for frequent query-document combinations, and batching optimisation for throughput — reducing reranking latency to within your production budget without precision degradation. Reranking latency optimisation is not simply choosing a smaller model — quantisation of a large model often outperforms a small model at the same latency budget with higher precision, adaptive reranking that dynamically adjusts depth per query reduces average latency without affecting precision for queries requiring deep reranking, and batching optimisation improves throughput without affecting per-query latency.

The optimisation covers INT8 quantisation of your reranker, adaptive depth implementation, result caching layer, batching optimisation, latency benchmarking at p50/p95/p99, and precision comparison confirming no quality regression.