What you'll learn:
- Build Spring Boot applications powered by Spring AI
- Integrate Spring AI app with OpenAI, Ollama, Docker Model Runner, and AWS Bedrock
- Use prompt templates and prompt stuffing techniques
- Convert AI text responses to Java Beans, Lists, and Maps
- Understand how LLMs work internally with tokens and embeddings
- Implement Retrieval-Augmented Generation (RAG) with Spring AI
- Implement memory in chat apps using Spring AI advisors
- Teach LLMs to call tools exposed by Java methods
- Build both MCP clients and servers with Spring AI
- From Testing to Production – Making AI Answers Safer with Evaluators
- Observability in Spring AI – Metrics, Monitoring & Tracing
- Transcription, Speech, and Image Generation using Spring AI
Are you ready to build AI-powered Java applications with real-world use cases? This hands-on course will teach you how to integrate cutting-edge AI capabilities into your Spring Boot applications using the Spring AI framework and OpenAI.
You’ll master everything from building your first chat-based app to using Retrieval-Augmented Generation (RAG), Tool Calling, Structured Output Conversion, MCP (Model Context Protocol), and even Speech-to-Text, Text-to-Speech, and Image Generation — all using Java and Spring Boot.
From understanding how LLMs work to deploying production-ready AI features with observability, testing, and advisor-based safety, this course is packed with powerful demos, clean explanations, and practical techniques to bring intelligence to your backend.
Whether you're a Java developer, Spring enthusiast, or backend engineer exploring Generative AI, this course will guide you step-by-step with best practices and battle-tested code.
What You’ll Learn:
Section 1: Welcome & Hello World with Spring AI
Understand the Spring AI framework and course roadmap
Build your first Spring Boot AI app using OpenAI
Deep dive into ChatModel and ChatClient APIs
Section 2: Prompt Engineering & Structured Output
Use message roles, prompt templates, and stuffing techniques
Work with advisors to control AI behavior
Map AI responses to Java Beans, Lists, and Maps
Section 3: Generative AI & LLM Fundamentals
Learn about tokens, embeddings, and how LLMs generate text
Understand attention, vocabulary, and model internals
Explore static vs positional embeddings and context windows
Section 4: AI Memory with ChatHistory
Implement stateless-to-stateful conversations
Use MemoryAdvisors and Conversation IDs for per-user memory
Persist chat memory using JDBC and configure maxMessages
Section 5: RAG – Retrieval-Augmented Generation
Set up a vector store (Qdrant) using Docker
Store and query document embeddings in Spring Boot
Use RetrievalAugmentationAdvisor to feed documents to AI
Section 6: Tool Calling – Let AI Take Action
Enable tool invocation via LLMs
Build tools for real-time actions like querying time or database
Customize tool errors and return responses to users
Section 7: Model Context Protocol (MCP)
Learn MCP architecture and communication patterns
Build MCP Clients and Servers using Spring AI
Integrate with GitHub’s MCP Server and explore STDIO transport
Section 8: Testing & Validating AI Outputs
Use RelevancyEvaluator and FactCheckingEvaluator
Test AI responses for correctness in dev and production
Add runtime safety checks with Spring Retry
Section 9: Observability – Monitoring AI Operations
Enable Spring Boot Actuator metrics for AI
Set up Prometheus & Grafana dashboards
Trace AI behavior with OpenTelemetry and Jaeger
Section 10: Speech & Image Generation
Convert voice to text with AI-powered transcription
Generate natural speech from text prompts
Turn prompts into images using the ImageModel
