Refine Retrieval Quality with Pinecone Rerank
Improving retrieval quality can be difficult when building vector search or retrieval augmented generation (RAG) at scale. Every increase in quality matters for a great user experience.
In most cases, rerankers require only a few lines of code and a bit of latency in exchange for significantly more relevant results. And with Pinecone's new Rerank offering, it's as easy as one more API call.
In this article, we will delve into the mechanics of rerankers, demonstrate how to integrate Pinecone Rerank into your search and RAG workflows and discuss optimal strategies for applying rerankers to your applications.
Rerankers refine the results you get from a vector database query. They do this by calculating relevance scores for each document based on how important it is to satisfy the user's query. That score is used to reorder the queried documents and return only the top-n results.
These points compound at scale: every second counts for quickly satisfying user queries in domains such as e-commerce, customer support, and finance.
