🤖 AI/ML Advanced ⏱️ 9 min

Agentic RRF Ensembling for Production Search

How BlueRobin combines multiple retrieval strategies and uses reciprocal rank fusion to produce more stable, high-quality contexts for LLM responses.

By Victor Robin • • Updated:
ragrrfensemblingagenticretrieval

Introduction

Different retrieval methods fail differently. BlueRobin uses ensembling with reciprocal rank fusion (RRF) so semantic, keyword, and graph-biased candidates contribute to final ranking.

Practical Pattern

  1. Generate candidate lists from each retriever.
  2. Normalize candidate IDs across sources.
  3. Apply weighted RRF scoring.
  4. Return top contexts with source metadata.

Signals from Recent Work

  • GraphRAG and hybrid retriever rollout increased retrieval path diversity.
  • Event contract updates stabilized downstream indexing and retrieval freshness.
  • NER ensembling improved entity-aware query expansion quality.

Conclusion

RRF is a pragmatic way to ensemble heterogeneous retrievers while keeping ranking interpretable and robust in production.

Related reading:

  • /hybrid-search-semantic-keyword/
  • /improving-rag-query-quality-and-relevance/