Chunking and Hybrid Search Shape RAG Quality in SaaS Products, Guide Finds

A guide drawing on two years of production RAG deployments argues that practical success depends on a few key decisions. It states that chunking strategy accounts for most retrieval quality, since fixed-size chunks break semantic units like sentences and table rows. The article reports that embedding model choice matters more than the choice of vector database, and that hybrid dense-plus-sparse search generally beats either method alone. It also describes reranking as offering the largest quality gain for the least computational cost. The piece explains that RAG grounds LLM answers in an organization's own data to reduce hallucinations, and is most useful for document Q&A and product knowledge tasks.