Retrieval RAG Evaluation Rita Fernandes Neves Senior Solution Architect - AI at NVIDIA Bilge Yücel DevRel Engineer Optimize RAG Applications with Document Reranking Using Haystack With NVIDIA NeMo Retriever March 20, 2025

Optimize RAG Applications with Document Reranking Using Haystack With NVIDIA NeMo Retriever In retrieval-augmented generation (RAG) applications, the quality of the retrieved documents plays a critical role in delivering accurate and meaningful responses. But what happens when embedding similarity is not enough to get an accurate ordering of the reference documents? This is where reranking comes into play. What’s Reranking? Reranking refers to assigning a relevance score to each document based on how well it matches the query. Reranking reorders the retrieved documents to ensure the most contextually relevant results are at the top. This is important because while the retrieval stage focuses on recall, considering relevance broadly, reranking “fine-tunes” the results for increased precision. Examples of Reranking Consider a query like, “What are the best practices for securing a REST API?” The retrieval model might return a ranked list…