- In short
- Hybrid retrieval combines dense and sparse methods, then merges and re-ranks the combined candidate set. Re-ranking with a second-stage model improves precision over either single method's raw ranking. Hybrid approaches are most valuable when the query mix includes both conceptual questions and exact-term lookups, but they add latency and complexity, so they are not automatically worth it for a narrow, uniform query pattern.
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