In Retrieval-Augmented Generation (RAG) systems, the efficiency of finding relevant information from a large knowledge base is critical. This post breaks down the core retrieval architecture, focusing on two key components: Embedding models and Rerank models. Embedding models convert text into dense vectors, enabling fast semantic similarity search. However, initial retrieval results can be noisy. Rerank models step in to refine these results, using more sophisticated cross-encoder architectures to reorder candidates and improve precision. The post explains how these two stages work together: first, a broad set of candidates is retrieved using embeddings; then, a reranker narrows down the list to the most relevant items. This two-stage approach balances speed and accuracy, making it a standard pattern in production RAG systems. For developers building AI applications, understanding this pipeline is essential for optimizing retrieval quality and overall system performance.
A practical guide to vector retrieval in RAG systems, explaining the complementary roles of embedding and reranking for improved search accuracy.