Building a RAG prototype is easy; taking it to production is not. We analyze chunking strategies, embedding selection, reranking, and architectures with Pinecone, Weaviate, and pgvector.
Retrieval-Augmented Generation (RAG) is the most popular architecture for making language models work with a company's internal data. However, moving from a functional prototype to a robust production system presents significant challenges: chunking quality determines response relevance, embedding selection directly impacts semantic accuracy, and reranking can improve results by up to 30%. In this article, we explore each of these components and share the configurations that have worked best for us with Pinecone, Weaviate, and pgvector.