5 hours agoDevelopmentProduction-Ready Text-to-SQL with Prompt Design, Schema Control, SQL Validation, and Safe LLM Integration
Course Description
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.
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