RAG systems grounded in structured data (relational databases, CSV, spreadsheets) can power more accurate, hallucination-resistant AI applications for enterprises. Structured RAG works by querying SQL databases, converting rows into vector embeddings tagged with metadata, and injecting results into LLM prompts via schema-grounded formats like JSON. Key benefits include reduced hallucinations, real-time data updates via change data capture (CDC), and lower LLM costs through smaller context windows. Challenges include translating natural language queries into SQL, keeping data fresh, and preventing prompt injection. The post covers implementation steps with Python/PostgreSQL code examples, use cases (BI assistants, customer service, fintech analytics, internal copilots), comparisons with knowledge graphs and data lakes, and relevant tooling including vector stores, hybrid search engines like Meilisearch, and orchestration frameworks like LangChain and LlamaIndex.
