From Java Dev to AI Engineer: Spring AI Fast Track from 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

What's inside

Learning objectives

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
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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
Show more
Show less