RAG Explained: Reranking for Better Answers

Reranking significantly enhances retrieval-augmented generation (RAG) systems by prioritizing the most relevant documents retrieved from large corpora, thereby improving the quality and accuracy of generated responses. This process involves applying sophisticated scoring models, such as learned neural rerankers, to reorder candidate documents before they are fed into the language model, leading to more precise and contextually appropriate answers.