Hybrid Search and Re-Ranking in Production RAG

Dense retrieval alone can fail in production RAG systems because it struggles with exact keyword matching, leading to incorrect or irrelevant answers. This happens when embedding models prioritize semantic similarity over specific terms, causing crucial documents to be ranked too low. To address this, hybrid search combines dense vector search with traditional keyword methods like BM25, which excels at term frequency and rarity. The balance between these two methods can be tuned using an alpha parameter and evaluated with metrics like hit rate and Mean Reciprocal Rank (MRR). For even better results, a re-ranking step using a cross-encoder can be added to precisely score the top retrieved documents and ensure the most relevant one is prioritized for the LLM.