Hybrid Search Fusion Tuning —Optimise Keyword & Semantic Weighti

I will tune the fusion weighting of your hybrid search system — running systematic experiments across fusion weight configurations (α for semantic, 1-α for keyword), evaluating each configuration on your query set, applying statistical significance testing to identify genuine improvements, and delivering the optimal configuration with supporting evidence. Default hybrid search fusion weightings are arbitrary — most implementations use equal weighting or a manufacturer-recommended default that is not calibrated to your specific query distribution; systematic tuning on your query set consistently improves retrieval quality by 5-15% over uncalibrated defaults.

The tuning covers fusion weight parameter space exploration, evaluation against your labelled query set, statistical significance testing across configurations, sensitivity analysis identifying how robust the optimal configuration is to small parameter changes, and a configuration recommendation with supporting evidence.