RAG for structured data: benefits, challenges, examples, & more

This post details how structured data can be integrated into RAG systems, including benefits like reduced hallucinations and real-time updates, challenges such as mapping semantic queries to structured ones, and implementation strategies. It highlights Meilisearch's capability to index structured records and provide filtered semantic search, enabling hybrid search with filters and semantic matching. Examples of structured RAG applications include BI assistants, customer service help, fintech analytics, and internal copilots. The post also differentiates structured RAG from knowledge graphs and data lakes.