Retrieve-then-rerank boosts RAG accuracy with dual-encoder approach

A technique called retrieve-then-rerank can significantly improve the accuracy of retrieval-augmented generation (RAG) systems by using a two-stage process. The first stage employs a fast bi-encoder to retrieve a broad shortlist of relevant documents. The second stage then uses a slower but more precise cross-encoder to re-rank this shortlist, ensuring the most relevant document is at the top for the LLM to process. This approach balances recall and precision, leading to more accurate and grounded AI responses. AI

IMPACT Enhances RAG system accuracy by ensuring the LLM receives the most relevant document chunk, improving grounding and response quality.

RANK_REASON The item describes a novel technique for improving RAG systems, detailing the architecture and implementation of bi-encoders and cross-encoders. [lever_c_demoted from research: ic=1 ai=1.0]

Read on dev.to — LLM tag →

• all-MiniLM-L6-v2
• bge-reranker
• bi-encoder
• Cohere Rerank
• cross-encoder
• cross-encoder/ms-marco-MiniLM-L-6-v2
• retrieval-augmented generation
• Retrieve-then-rerank
• SentenceTransformer
• Voyage rerank

• infra
• model release

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