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:
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A Text-to-SQL API using Spring AI
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Schema-aware prompt design to improve SQL accuracy
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Dynamic schema discovery from PostgreSQL at runtime
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AST-based SQL validation to block unsafe queries
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Table and column validation using real schema
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LIMIT enforcement and execution gating
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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:
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Design a clean Text-to-SQL architecture in Spring Boot
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Control LLM behavior using schema, prompts, and backend logic
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Discover and manage database schema dynamically
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Prevent dangerous SQL from ever reaching your database
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Integrate a simple UI with a backend AI-powered API
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Understand where RAG is useful — and where it is not
Who this course is for
This course is designed for:
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Java and Spring Boot developers exploring real AI use cases
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Backend engineers who care about architecture and safety
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Developers comfortable with SQL who want to automate queries using AI
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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.
