Many developers building Retrieval-Augmented Generation (RAG) systems settle for mediocre results, often overlooking a simple fix: reranking. The standard bi-encoder approach sacrifices interaction signals by compressing queries and documents into isolated vectors. This causes issues, like missing the truly relevant document in a search for "cheap hotels in Tokyo" because embedding similarity isn't nuanced enough.

Cross-encoders solve this by concatenating the query and document, allowing full self-attention across all tokens before generating a relevance score. This two-stage pattern—fast bi-encoder retrieval followed by precise cross-encoder reranking—is now common, utilized by services like Cohere and Pinecone to boost result quality.

The trade-off for this precision is compute cost; cross-encoders require a full transformer pass for every candidate pair, making them infeasible for initial retrieval across large corpora. Instead, practitioners fine-tune pre-trained models, like those based on BERT, using relevance triples from datasets like MS MARCO.

This technique effectively addresses the limitations of independent vector encoding, providing a concrete path to better relevance without needing to train a massive language model from scratch. Fine-tuning on domain-specific data ensures the reranker understands local context, transforming 'okay' results into highly accurate ones.