What is Reranker?

How Reranker Works

A typical RAG pipeline retrieves the top-K documents using vector similarity. Vector similarity is fast but coarse; it can miss subtle relevance signals. A reranker takes the query and each retrieved document, scores them more carefully (often using a cross-encoder model that processes query and document together), and reorders. Cohere Rerank, Voyage Rerank, and open-source models like ms-marco-MiniLM are common choices. The reranker is slower than retrieval but processes only the top-K candidates, keeping costs manageable.

Why Reranker Matters

Rerankers are the most impactful single addition to a baseline RAG system. Adding a reranker typically improves answer quality by 15-30% on benchmarks. They're cheap to add (one API call or one inference per query) and produce visible quality gains. For any production RAG application, a reranker should be considered a baseline component, not an optimization.

Practical Example

An enterprise search team added Cohere Rerank to their RAG pipeline. Without rerank, top results were correct 67% of the time. With rerank, that improved to 89%. The added latency was 200ms per query, acceptable for the use case. The cost was negligible relative to LLM API costs.

Use Cases

• RAG systems
• Search improvement
• Document Q&A
• Knowledge base applications

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