Reranking

Simple Definition

Reranking is a second-pass filtering step used in AI search and retrieval. After an initial search retrieves a set of candidate results, a reranker model re-evaluates each result and reorders them based on how truly relevant they are to the query.

The goal: take a rough shortlist and make it more accurate before the AI uses it to generate an answer.

Why Reranking Exists

Initial retrieval, especially vector search, is fast but imprecise. It finds documents that are semantically similar to a query but may not be the most directly relevant. Reranking is slower but smarter: it scores each candidate based on how well it actually answers the specific question.

Without reranking, an AI answering questions based on retrieved documents may use sources that are vaguely related but not the best match, leading to weaker or less accurate answers.

How Reranking Works in a RAG Pipeline

User asks a question
A vector search retrieves the top 20–50 similar documents
A reranker model scores each of those documents against the question
The top 3–5 highest-scoring documents are selected
Those are passed to the AI model to generate an answer

The reranker acts as a quality filter between retrieval and generation.

Retrieval vs. Reranking

Step	Speed	Precision	Purpose
Vector retrieval	Fast	Lower	Cast a wide net
Reranking	Slower	Higher	Pick the best from the net

Where Reranking Is Used

RAG systems: improving which documents an AI uses to answer questions
Enterprise search: making search results more accurate
Customer support AI: finding the most relevant help articles
Legal and medical AI: prioritizing the most pertinent source documents

Reranking Models

Common reranking models include Cohere Rerank, BGE Reranker, and Jina Reranker. These are separate, lightweight models used specifically for scoring document-query relevance.

RAG, the broader pipeline where reranking improves results
Vector Database, where initial retrieval happens before reranking
Embedding, the method used to find candidates that reranking refines
Semantic Search, the search layer that reranking improves
Grounding, reranking helps ensure AI responses are grounded in the most relevant sources