Technology

rerank

Rerank employs a cross-encoder model to reorder initial retrieval results, maximizing precision by prioritizing the most contextually relevant documents for the query.

Reranking is the critical second stage in a modern two-stage retrieval system (RAG). The first stage, typically a fast vector search (BM25 or bi-encoder), aims for high recall, retrieving a broad set of candidate documents (e.g., 50-100 chunks). The reranker, often a more computationally intensive cross-encoder model (e.g., Cohere Rerank or a fine-tuned Mistral-7B), then analyzes the query and each document pair together. This deep, contextual analysis assigns a precise relevance score, reordering the candidate list to ensure the final top-N documents (e.g., 3-5) passed to the Large Language Model (LLM) are the most pertinent. This process dramatically improves the quality and accuracy of the LLM's final generated response.