Scaling Retrieval Augmented Generation with RAG Fusion: Lessons from an Industry Deployment
Retrieval-Augmented Generation (RAG) systems commonly adopt retrieval fusion techniques such as multi-query retrieval and reciprocal rank fusion (RRF) to increase document recall, under the assumption that higher recall leads to better answer quality. While these methods show consistent gains in isolated retrieval benchmarks, their effectiveness under realistic production constraints remains underexplored. In this work, we evaluate retrieval fusion in a production-style RAG pipeline operating over an enterprise knowledge base, with fixed retrieval depth, re-ranking budgets, and latency constraints. Across multiple fusion configurations, we find that retrieval fusion does increase raw reca...
| Authors | Luigi Medrano, Arush Verma, Mukul Chhabra |
|---|---|
| Primary category | cs.IR |
| Published | 2 March 2026 |
| Last updated at source | 2 March 2026 |
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