NFO
Mimir, Keeper of the Well of Wisdom
Spring AI Masterclass for Generative & Agentic AI Developers
https://www.udemy.com/course/spring-ai-masterclass-java-agentic-
generative-mcp-rag-openai-ollama/
Year : 2026
Language : English
Level : Intermediate Level
Category : Development
Subcategory : Programming Languages
Duration : 5h 42m
Lectures : 57
Rating : 4.7/5 (43 reviews)
Students : 277
INSTRUCTOR(S)
HEADLINE
Project driven, hands-on learning for Java developers who want
to build advanced, multi-modal, Agentic AI applications.
WHAT YOU'LL LEARN
* Build real AI applications with Spring Boot and Spring AI,
starting with a ChatGPT-style app that connects to modern
LLMs
in minutes.
* Learn how to turn messy AI responses into clean structured
Java objects your applications can safely process and
display.
* Build a powerful RAG system that lets your application
answer
questions using your own documents and knowledge.
* Create AI agents using the Model Context Protocol so your
apps
can call tools, APIs, and services automatically.
* Develop chatbots with memory that remember previous messages
and produce natural, contextual conversations.
* Learn how to test, evaluate, and observe AI prompts and
responses so your applications behave reliably in
production.
* Add advanced features like text-to-speech, image generation,
OCR, and language translation to your Spring AI apps.
* Build advanced, multi-modal, Spring AI apps that integrate
with voice, language translation and image analysis
* You'll finish the course with the skills to build
production-
ready AI features inside modern Java applications.
REQUIREMENTS
* Basic Java programming knowledge is required. Spring
Framework
knowledge is especially helpful.
WHO IS THIS COURSE FOR
* Software developers interested in acquiring the skills
required to implement advanced AI solutions in Java and
Spring.
DESCRIPTION
This isn't your typical Udemy course. That's one thing I can
assure you. A Better Way to Learn In this course, you won't
find
any long PowerPoint presentations or teleprompter driven
lectures that put you to sleep. That's a promise. I've always
believed that training has to be project driven, and that the
best learning happens when a developer gets hands on
experience
with the tools, technologies and APIs they have to master. I
have also always believed that conversations are better than
slideshows, and that the mantra that shapes this entire
course.
What This Course Covers So what do we cover in this course?
Quite frankly, we'll cover everything you need to know to
become
both capable and confident with Spring AI and the broader
ecosystem of LLM and MCP tools that power Generative AI and
Agentic AI applications. You'll learn about all of the most
important Agentic AI├┐skills that are in the most demand in
today's job market. The True Masterclass I'm honestly a little
surprised by how just how much this Spring AI├┐course covers.
We
move from high-level discussions about how to choose the right
machine learning model, to low-level concepts such as how
temperature and TopP influence system design and application
behavior. The True Masterclass I'm honestly a little surprised
by how just how much this GitHub Copilot├┐course covers. This
is
a hands-on masterclass packed with practical topics, tasks,
and
real projects. Here's a non-exhaustive list of the tools and
technologies covered in this course, along with the projects
that drive that learning: Spring AI and LLM Integration How to
build a ChatGPT-style clone that only talks about Simpsons
trivia How to connect to virtually any LLM, from OpenAI and
Gemini in the cloud to DeepSeek running locally with Ollama
How
to engineer prompts properly so your generative applications
produce better, more reliable results Real-World AI
Applications
How to create a social media post engine and even use AI
agents
to publish content automatically How to build chatbots with
long-running memory How to create multimodal applications that
handle text-to-speech and speech-to-text How to generate
images
programmatically with Spring AI How to go from image to text
using Spring AI OCR capabilities How to translate text from
one
language to another RAG, MCP, and Agentic AI How to implement
Retrieval Augmented Generation, and what to do when retrieval
quality falls short How to create Model Context Protocol
servers
and call them from MCP clients How to debug and troubleshoot
agentic applications How to detect drift and divergence in
your
LLM-powered systems What This Course Will Do for You This
masterclass will not teach you everything there is to know
about
Generative AI and Agentic AI. No course can. What it will do
is
teach you everything you need to know to explore new AI topics
with confidence, competence, and credibility, even when those
topics are not explicitly covered in the course. Who This
Course
Is For This course is built for Java developers who want to
move
into the AI space and do it the right way. It is designed to
help you get started, keep progressing, and maintain the pace
of
learning required to stay current with the rapid changes
happening across the Java, Spring, machine learning, and
Agentic
AI space. If you want a practical, project-driven course that
helps you build real skills instead of just watch slides, this
course is made for you.
COURSE CONTENT
Chapter 1: Introduction, Overview and Prerequisites
1. A Brief Course Overview
2. What are the Course Prerequisites and Why?
3. Installing an IDE with Spring Tools
4. How to Install GitHub Copilot in Eclipse (Optional)
5. Installing the JDK
6. Installing Your Build Tool (Apache Maven)
7. Installing Node (Optional)
Chapter 2: Day 1: Getting Started with SpringAI, OpenAI and
our own ChatGPT Simpsons Clone
8. Getting Started with LLMs || How to Get & Set Your OpenAI
API Key
9. Build a Spring Boot App with AI, OpenAI, Web & DevTools
Support
10. How to Autowire the Spring AI ChatClient Builder for
OpenAI
11. Query an OpenAI LLM from your Spring Boot App with
Spring AI and the ChatClient
12. Build a RESTful API with Spring AI Support
13. Smoke Testing our RESTful Endpoint
14. Prompt Engineering with System Messages, User Messages
and Spring AI
15. Best Practices for Managing your OpenAI, Gemini, Grok &
Anthropic API Keys
16. Logging System Prompts and User Prompts to the Console
17. Building the ChatGPT Clone's Web Interface with GitHub
Copilot
Chapter 3: Day 2: Structured Text, Customizing your LLM and
Generating Social Media Slop
18. Introduction to the AI Slop Generator Project
19. Building the Slop Generator Spring AI Project
20. The @RestController and the YAML Flavor of the
application.properties File
21. Working with Spring AI's PromptTemplate
22. Configuring the LLM Model's ChatOptions with Spring AI
23. LLM Temperature vs TopP Selection
24. Prompt Improvements
25. Structured LLM Output with Spring AI's
BeanOutputConverter
26. Building the AI Slop Generator's UI using SpringMVC &
GitHub Copilot
Chapter 4: Day 3: Retrieval Augmented Generation with Spring
AI and The Simpsons (RAG)
27. What is Retrieval Augmented Generation and What Problems
do RAG Agents Solve
28. How Retrieval Augmentation Works - A Simple Explanation
29. Why our RAG APP Needs a Vector Store
30. @RestControllers, VectorStores, EmbeddingModels and
Dependency Injection
31. Generating Embeddings with OpenAI Embedding Models
32. Putting the Retrieval, Augmentation and Generation into
the RAG App
33. Testing the Pickering is Springfield RAG Application &
Inspecting the Logs
34. Full RAG Web UI with GitHub Copilot
Chapter 5: Building a ChatBot with Spring AI
35. The Missing Link: Why Agentic Applications Need Memory
36. Working with Spring AI ChatMemory and ChatHistory
37. How to Use Spring AI Advisors
38. Updating the ChatBot's Web UI to Support Spring AI's
ChatMemory Advisor
Chapter 6: Day 5: Mastering Model Context Protocol (MCP) with
Spring AI
39. Why the LLM World Needs Model Context Protocol (And Why
You Need it Too!)
40. Creating your First MCP Server with Spring AI and the
@McpTool Annotation
41. Mastering MCP Clients and Spring AI's
ToolCallbackProvider
42. Building Resource Manipulation MCP Server Tools
43. Input, Output and Effective MCP Tool Calls and Prompts
Chapter 7: Testing, Debugging, Logging & the Observability of
Non-Deterministic AI Systems
44. The Challenges of Testing and Debugging Generative and
Agentic AI Applications
45. Testing MCP Server Tools with the MCP Inspector
46. Drift, AI Testing and Using LLMs as a Judge
47. Detecting Drift in your LLMs with JUnit and Spring AI's
FactCheckingEvaluator
48. Custom Log Settings
49. Spring AI Observation with Actuators, Prometheus and
Metrics Gathering
Chapter 8: Day 7: Final Project with OCR, Text-2-Speech,
Structured Text & Image Generation
50. Introducing the MagicMenu Generative & Agentic AI
Application
51. Configuring the Text-to-Speech Attributes in
application.properties (Use Alloy!)
52. Initializing the Spring AI App's ImageModel and the
TextToSpeechModel
53. OCR Made Easy with Spring Boot and OpenAI
54. Adding Translation Capabilities to our Spring AI App
55. Transforming Translations into Structured Text and JSON
Entities
56. Image Generation with Spring AI and the DALL├║E
ImageModel
57. Mastering Text to Speech
DATES
Published : 2026-04-09
Last Updated : 2026-04-08
If you fear the truth, dont come to my well.
CRC32: ec2e9c3a6f5a1416c16344026a4027d2d9dd4a4b