Why You Should Use a Reranker to Improve RAG Accuracy

Standard vector search (Bi-Encoders) is fast, but it often misses the most relevant documents because it only looks at semantic similarity. A "Reranker" (Cross-Encoder) is the secret weapon for taking RAG accuracy to the next level.

The Two-Stage Retrieval Process

In a high-accuracy RAG system, you first use vector search to find the top 50-100 candidates. You then pass these candidates, along with the user's query, into a Reranker model. The Reranker performs a much more intensive analysis of the relationship between the query and each document, re-scoring them to find the true "best" matches.

Filtering Out the Noise

Vector search can be easily fooled by similar-sounding but irrelevant text. A Reranker acts as a "second opinion" that can distinguish between a document that merely uses similar words and one that actually contains the answer to the user's question. Adding this extra step ensures that the AI's context window is filled with only the most high-fidelity information.