Reranking Emerges as RAG Best Practice
Reranking is now considered a foundational technique in enterprise search and RAG pipelines, according to a recent guide. The best practice involves using cross-encoders as a second-stage filter on initial results from a vector database to significantly improve precision. This two-stage approach balances the speed of initial retrieval with the high relevance required for enterprise applications.
The initial retrieval stage in a RAG pipeline often uses a hybrid approach, combining keyword-based (sparse) methods like BM25 with semantic (dense) vector search. This dual strategy improves recall, ensuring that documents containing exact keywords are captured alongside those that are only semantically related, casting a wider net for the reranker to refine. This initial step is designed for speed and is crucial for handling the scale of enterprise data. Cross-encoders are computationally expensive because they must process the query and each candidate document together as a pair, which is why they are not used on the entire dataset. This joint processing allows the model's attention mechanism to analyze the interaction between every token in the query and every token in the document, leading to a much more nuanced relevance score than the initial retrieval's standalone vector comparison. Performance benchmarks demonstrate significant gains from this two-stage process, with one study showing an average accuracy improvement of over 33% across datasets like MS MARCO and Natural Questions. Commercial models from providers like Cohere have shown up to a 30.8% improvement over traditional BM25 search in financial domains. Key evaluation metrics for this stage include Mean Reciprocal Rank (MRR) and Normalized Discounted Cumulative Gain (NDCG), which measure how high up the correct results are ranked. For implementation, engineers can use open-source libraries like Sentence Transformers, Haystack, or LlamaIndex to integrate reranking into their pipelines. Popular open-source models include the `bge-reranker` series and the `ms-marco-MiniLM` models, while Cohere's Rerank API and Voyage AI offer state-of-the-art proprietary solutions. Newer architectures like ColBERT use a "late interaction" mechanism to balance the speed of bi-encoders with the accuracy of cross-encoders. In the competitive enterprise search market, this pattern is already an established practice. Glean, a major competitor, explicitly details its use of a hybrid retrieval and reranking system that combines dozens of signals, including semantic similarity and keyword match, to score and surface the most relevant information for each employee. This highlights how sophisticated, multi-stage retrieval is a key differentiator for delivering relevance at enterprise scale.
