# Text-to-SQL with RAG: Building a Chatbot That Talks to Your Database > Source: > Published: 2026-08-01 22:01:01+00:00 Member-only story # Text-to-SQL with RAG: Building a Chatbot That Talks to Your Database *How to use RAG with structured data — a hands-on POC that converts plain English into safe, verified SQL, with real examples of what breaks and how to fix it.* Almost every RAG example you see online uses unstructured data. PDFs, wikis, support articles — chunk them, embed them, retrieve them, done. But here’s the thing: most of the data a business actually cares about doesn’t live in documents. It lives in relational databases — customers, orders, invoices, products. So I set out to answer a simple question: **how does RAG work with structured data?** Can a chatbot answer questions from a database just as naturally as it answers from documents — so that one day, the same bot can pull from both? This POC is my answer. The core technique is **text-to-SQL**: the user asks in plain English, and the system converts it into a safe, correct SQL query. It sounds simple. The first version I built always gave an answer — it just wasn’t always the *right* answer, and it never told me when it was guessing. Everything in this post is about closing that gap. The full source code is open — link at the end. Everything runs locally with Python, SQLite, and an OpenAI API key. No vector database, no Docker, no cloud setup.