From Java Dev to AI Engineer: Spring AI Fast Track | Udemy

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