Name: From Java Dev to AI Engineer: Spring AI Fast Track
Rating: 4.7 (2203 reviews)

Udemy Business

Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Created byMadan Reddy, Eazy Bytes

Last updated 5/2026

English

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

11 sections • 101 lectures • 13h 55m total length

Course Introduction10:02
Details of Source Code, PDF Content & other instructions for the course1:07
What is Spring AI Framework5:35
Explore Spring AI framework that bridges Spring Boot with LLM models, enabling multi-provider LLM access, MCP protocol, vector store integration, and guardrails for reliable AI-powered apps.
IntelliJ IDEA Ultimate Subscription0:59
Hello World Spring AI app with OpenAI - Part 111:46
Hello World Spring AI app with OpenAI - Part 215:55
Building a “Hello World” App with Spring AI and Ollama12:36
Building a “Hello World” App with Spring AI and Docker11:13
Building a “Hello World” App with Spring AI and AWS Bedrock14:03
Working with Multiple Chat Models in Spring AI12:11
Choosing the Right LLM Deployment Strategy for Spring AI

Understanding Message Roles in LLMs - Theory6:24
Understanding Message Roles in LLMs - Demo8:56
What Are Defaults in Spring AI10:09
Using Prompt Templates in Spring AI12:39
Using Prompt Stuffing technique9:24
Why Prompt Stuffing Isn’t Meant for Big Data6:11
Understanding Advisors in Spring AI Workflows4:44
Spring AI Built-in Advisors - Plug and Play Intelligence10:27
Configure default advisor method and custom advisors for spring ai by using chartClientBuilder, log requests and responses with SimpleLoggerAdvisor, and implement CallAdvisor and StreamAdvisor for normal and streaming llm interactions.
Custom Advisors in Spring AI - Make the AI Work Your Way9:45
Understanding ChatOptions in Spring AI7:31
Configuring ChatOptions in Spring AI10:20
Spring AI ChatClient – Response Types Explained5:59
Streaming AI Responses in Real-Time using stream() Method4:43
Implement streaming AI responses in real-time with a Spring stream() API backed by Flux, enabling non-blocking delivery of LLM outputs and live browser demonstrations.
From Text to Types - Mastering Structured Output in Spring AI6:25
Explore how Spring AI's structured output converter adds formatting instructions and converts LLM responses into structured data like JSON or Java objects, enabling easy integration with business logic.
From AI Text to Java Objects - Spring AI Structured Output Demo7:40
Using Bean, List, and Map Output Converters in Spring AI5:40
Mapping AI Response to List<POJO> using ParameterizedTypeReference4:21
Ensuring Structured Outputs in Spring AI Applications

Tracing the Roots - How We Reached Generative AI4:42
Meet the Gen AI Family - AI, ML, DL, and Beyond10:53
Funny memes of AI5:11
Types of Generative AI Models3:18
Introduction to Large Language Models - The Text Experts10:08
Core job of an LLM - Guessing next word7:19
The concept of Tokens & Token IDs in LLM8:27
Inside an LLM’s Dictionary - Understanding Model Vocabulary8:58
Embeddings & Vectors - A way to represent meaning7:13
How Embedding Vectors calculated11:26
What are Static Embeddings5:51
Positional Embeddings - How AI Understands Word Order4:22
The Magic of Attention - How AI Understands Context7:58

LLMs Don’t Remember You – The Stateless Nature of AI Models5:51
LLMs Forget Everything — ChatMemory to the Rescue!7:42
Making ChatClient ‘Remember’ – Spring AI’s Memory Advisors6:23
From Stateless to Stateful - Spring AI Memory in Action8:36
Per-User Memory in LLMs – Thanks to CONVERSATION_ID!5:02
Chat Memory That Lasts – Spring AI with JDBC-Based Persistence12:43
Using maxMessages to Limit Chat History in Spring AI5:02
Avoiding Token Overload - Use maxMessages with Context Window size in Mind6:55
Adding Memory to LLM Conversations

From Prompt Stuffing to RAG – A Better Way to Feed LLMs5:17
Explore how the Rag framework enhances llm responses by feeding relevant context from a vector database–backed knowledge base, addressing private data limitations and enabling personalized, accurate results.
Understanding the RAG flow6:32
Meet the Vector Database - Storing Meaning, Not Just Data7:26
Setting Up Qdrant Vector Store with Spring Boot Using Docker Compose8:11
Demo of RAG using random text data - Part 19:32
Demo of RAG using random text data - Part 29:08
Demo of RAG using random text data - Part 35:17
Let's talk to documents using RAG12:57
Breaking Down Big Documents - The Art of Chunking for LLMs5:10
RAG Made Easy - RetrievalAugmentationAdvisor in Action4:58
Explore retrieval augmented generation with the RetrievalAugmentationAdvisor to query a vector store, tune top k and similarity thresholds, and automatically populate the system prompt in REST APIs.
RAG flow with Web Search - Part 16:30
RAG flow with Web Search - Part 214:56
Advanced RAG with Pre-Retrieval11:30
Advanced RAG with Post-Retrieval7:33
Who calculates the embedding values of the documents9:03
Enhancing Answers with External Knowledge/Private Documents

Introduction to Tool Calling in AI5:24
LLMs vs Reality - The Time Question They Can’t Answer6:09
Tool Calling in Action - Teaching LLMs to Tell the Time10:02
How the Tools get called by LLM internally11:46
Letting LLMs Talk to Databases - A Tool Calling Demo - Part 110:12
Letting LLMs Talk to Databases - A Tool Calling Demo - Part 210:31
Letting LLMs Talk to Databases - A Tool Calling Demo - Part 38:01
Watch llms talk to databases through a tool calling demo, creating and querying HELPDESK_TICKETS in an h2 database, with rest api testing via postman.
Bypass the Model - Returning Tool Results Directly to the User5:25
Graceful Failure - Customizing Tool Error Responses in Spring AI4:55
Generative AI’s Journey - From Static Chat to Dynamic Intelligence7:26
Trace the evolution of generative ai from llm-based knowledge to rag and tool calling. Explore how Spring AI framework enables building intelligent applications and ai agents that act autonomously.

The Missing Link in AI Apps - Understanding the Need for MCP3:52
Inside MCP - Understanding the Architecture of Model Context Protocol10:34
Behind the Scenes of MCP Communication - Built-in Transport Logic9:31
Building an MCP Client with Spring AI15:15
Demo of MCP Client and MCP Server integration4:57
Meet the MCP Inspector - The X-Ray Tool of MCP Server7:00
Demo of integration with GitHub MCP Server13:18
Building an MCP Server using STDIO - Part 114:37
Building an MCP Server using STDIO - Part 210:51
Building an MCP Server using remote invocation9:14
Standardizing AI Tool Integrations

Testing the Untestable - Evaluating LLM Responses with Spring AI6:50
Building Trustworthy AI with RelevancyEvaluator and FactCheckingEvaluator10:54
RelevancyEvaluator Demo - Ensuring LLMs Stay on Topic14:12
FactCheckingEvaluator in Action - Catching AI Hallucinations11:21
Using Spring AI Evaluator in a RAG Scenario4:23
Catching Bad LLM Answers in Production with Runtime Checks8:44
Runtime Validation with Spring AI Evaluator and Spring Retry8:44
Making AI Testing More Reliable

Introduction to monitoring AI Operations4:07
Enable observability in a Spring AI application by exposing metrics and tracing via the actuator, and monitor AI operations across logs, metrics, and tracing.
Enabling Metrics in Spring AI with Spring Boot Actuator - Part 113:02
Enabling Metrics in Spring AI with Spring Boot Actuator - Part 25:08
Enabling Prometheus Monitoring in Spring AI - Part 18:38
Enabling Prometheus Monitoring in Spring AI - Part 25:12
Setting Up Grafana Dashboard for Spring AI Metrics10:20
Set up Grafana with docker, connect to Prometheus in the spring-ai network, and persist dashboards with grafana-storage while building token usage and tool metrics dashboards on port 3000.
Tracing AI Operations using OTLP & Jaeger14:48

Requirements

Knowledge on Java, Spring Boot is mandatory

Description

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

Who this course is for:

Java and Spring Boot developers eager to integrate AI into real-world applications
Backend developers curious about LLMs, prompt engineering, and AI-powered workflows
Full Stack developers interested in adding AI capabilities to their microservices or APIs
Architects exploring Retrieval-Augmented Generation (RAG) and Tool Calling in Spring ecosystems
Professionals aiming to bring natural language interfaces to enterprise applications
Devs building chatbots, voice assistants, or image generation tools using Spring AI
Students and enthusiasts who want a practical, hands-on approach to Generative AI with Java

What you'll learn

Explore related topics

Course content

Spring AI – Say Hello to AI in Spring Boot11 lectures • 1hr 35min

Spring AI Essentials - Prompts, Advisors, and Structured Responses18 lectures • 2hr 11min

Foundations of Generative AI and LLMs13 lectures • 1hr 36min

Teaching LLMs to Remember - The Power of Chat Memory in Spring AI9 lectures • 58min

The Art of Talking to Documents – RAG Unleashed16 lectures • 2hr 4min

Tool Calling in Action - Giving LLMs the Power to Do Things10 lectures • 1hr 20min

Mastering Model Context Protocol (MCP)11 lectures • 1hr 39min

From Testing to Production – Making AI Answers Safer with Evaluators8 lectures • 1hr 5min

Observability in Spring AI - Metrics, Monitoring & Tracing7 lectures • 1hr 1min

Transcription, Speech, and Image Generation using Spring AI3 lectures • 24min

Requirements

Description

Who this course is for: