Spring AI Text-to-SQL: Turning Questions into SQL with LLMs

Text-to-SQL is one of the most powerful real-world use cases for Large Language Models. The idea is simple: a user asks a question in plain English, and the system generates and executes SQL automatically.

> Doing this with ChatGPT is easy.

> Doing this safely and correctly inside a backend system is not.

This course teaches you how to build a complete, production-style Text-to-SQL system using Spring AI, Spring Boot, and PostgreSQL, with clear architecture, strong backend control, and zero reliance on “AI magic”.

You will not build a chatbot.
You will not build a dashboard.

You will build a backend system that you could confidently use at work.

Includes professionally prepared subtitles in Spanish, Portuguese (Brazil), Japanese, and Chinese.

Includes free 90-day access to IntelliJ IDEA Ultimate for a professional development experience.

What makes this course different

Most AI + SQL demos you see online follow this pattern:

User question → LLM → SQL → Database

This course shows why that is dangerous, and how to design the system properly:

User question → Spring Boot backend → LLM → SQL validation → Database

The LLM suggests.
The backend controls everything.

What you will build

Throughout the course, you will work on a single Spring Boot project that evolves module by module. Instead of toy examples, you will use a realistic company database (employees, projects, customers, orders, invoices, payments) so queries feel like real systems.

You will build:

• A Text-to-SQL API using Spring AI

• Schema-aware prompt design to improve SQL accuracy

• Dynamic schema discovery from PostgreSQL at runtime

• AST-based SQL validation to block unsafe queries

• Table and column validation using real schema

• LIMIT enforcement and execution gating

• A simple UI that consumes the API and displays results and errors

By the end, you will have a working system where a plain English question turns into safe, validated SQL and real database results.

What you will learn

You will learn how to:

• Design a clean Text-to-SQL architecture in Spring Boot

• Control LLM behavior using schema, prompts, and backend logic

• Discover and manage database schema dynamically

• Prevent dangerous SQL from ever reaching your database

• Integrate a simple UI with a backend AI-powered API

• Understand where RAG is useful — and where it is not

Who this course is for

This course is designed for:

• Java and Spring Boot developers exploring real AI use cases

• Backend engineers who care about architecture and safety

• Developers comfortable with SQL who want to automate queries using AI

• Engineers who want practical AI integration, not demos

This course is not focused on frontend development, dashboards, or prompt-only experiments.

The end result

By the end of this course, you will understand how to integrate LLMs into backend systems in a controlled, production-ready way and build a safe Text-to-SQL system from scratch using Spring AI.