Free Download Spring AI Text-to-SQL Turning Questions into SQL with LLMsPublished 2/2026
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Language: English |
Duration: 2h 5m |
Size: 1.46 GB
Production-Ready Text-to-SQL with Prompt Design, Schema Control, SQL Validation, and Safe LLM Integration
Build a complete Text-to-SQL backend in Spring Boot using Spring AI
Design schema-aware prompts that improve SQL accuracy without relying on RAG.
Dynamically discover database schema at runtime and use it in LLM prompts.
Implement AST-based SQL validation to safely execute only trusted queries.
Enforce limits, table/column checks, and prevent unsafe SQL from reaching the database.
Integrate a simple UI with the API to visualize SQL generation, results, and validation errors.
RequirementsBasic knowledge of Java and Spring Boot is required.
Familiarity with SQL and relational databases (PostgreSQL preferred).
Understanding of REST APIs and JSON requests/responses.
No prior knowledge of AI, LLMs, or frontend development is required.
DescriptionText-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.What makes this course differentMost AI + SQL demos you see online follow this pattern:User question → LLM → SQL → DatabaseThis course shows why that is dangerous, and how to design the system properly:User question → Spring Boot backend → LLM → SQL validation → DatabaseThe LLM suggests.The backend controls everything.What you will buildThroughout 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 AISchema-aware prompt design to improve SQL accuracyDynamic schema discovery from PostgreSQL at runtimeAST-based SQL validation to block unsafe queriesTable and column validation using real schemaLIMIT enforcement and execution gatingA simple UI that consumes the API and displays results and errorsBy 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 learnYou will learn how to:Design a clean Text-to-SQL architecture in Spring BootControl LLM behavior using schema, prompts, and backend logicDiscover and manage database schema dynamicallyPrevent dangerous SQL from ever reaching your databaseIntegrate a simple UI with a backend AI-powered APIUnderstand where RAG is useful - and where it is not
Who this course is forThis course is designed for:Java and Spring Boot developers exploring real AI use casesBackend engineers who care about architecture and safetyDevelopers comfortable with SQL who want to automate queries using AIEngineers who want practical AI integration, not demosThis course is not focused on frontend development, dashboards, or prompt-only experiments.The end resultBy 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.
Who this course is forJava and Spring Boot developers exploring real AI use-cases in backend systems.
Backend engineers who want safe, production-style integration of LLMs.
Developers comfortable with SQL who want to automate database queries using AI.
Engineers who care about architecture, validation, and control - not AI demos.
Anyone curious how to use Spring AI in a practical, backend-first way.
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