Retrieve and gain: 3 RAG use cases you can ship today

This post details three practical RAG use case patterns: Retrieval-first for commercial flows (e.g., e-commerce), Retrieval + Generation for answering internal questions (e.g., policy/product queries), and Growth/Infrastructure for scaling RAG systems. It highlights how Meilisearch's unified hybrid search engine simplifies scaling by handling both lexical and semantic recall in one engine, reducing operational complexity compared to separate keyword and vector stores. It provides examples of how teams leverage Meilisearch for improved retrieval quality (e.g., Bookshop.org's 43% lift in search-to-purchase conversion), concise sourced answers (e.g., Louis Vuitton store associates), and scalable infrastructure (e.g., Hugging Face across 2.2M model cards). The post emphasizes metrics like search-to-purchase conversion, zero-results queries, and the importance of source-aware retrieval and snippet retrieval for generation.