How do you choose weights for dense and sparse score fusion in hybrid search?

If you use Reciprocal Rank Fusion, no weight tuning is needed since RRF combines ranks rather than scores. For weighted score fusion, normalize each retriever's scores to a comparable range, then try alpha values from 0.2 to 0.8 on a labeled evaluation set to find the sweet spot. Dense weight tends to dominate for semantic queries and natural-language questions, while sparse weight helps on queries with proper nouns, product identifiers, or technical terms. Many systems tune weights per query intent using a classifier. ---