Build AI Apps with Spring AI, OpenAI, RAG, MCP, AI Testing, Observability, Speech & Image Generation
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.
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.
Course Content
- Spring AI – Say Hello to AI in Spring Boot –> 11 lectures • 1hr 34min.
- Spring AI Essentials – Prompts, Advisors, and Structured Responses –> 17 lectures • 2hr 10min.
- Foundations of Generative AI and LLMs –> 13 lectures • 1hr 36min.
- Teaching LLMs to Remember – The Power of Chat Memory in Spring AI –> 8 lectures • 59min.
- The Art of Talking to Documents – RAG Unleashed –> 11 lectures • 1hr 24min.
- Tool Calling in Action – Giving LLMs the Power to Do Things –> 10 lectures • 1hr 20min.
- Mastering Model Context Protocol (MCP) –> 10 lectures • 1hr 38min.
- From Testing to Production – Making AI Answers Safer with Evaluators –> 7 lectures • 1hr 3min.
- Observability in Spring AI – Metrics, Monitoring & Tracing –> 8 lectures • 1hr 1min.
- Transcription, Speech, and Image Generation using Spring AI –> 4 lectures • 38min.
- Thank You & Congratulations –> 1 lecture • 1min.
Requirements
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