Complete GenAI with Java & Spring AI: LLMs, RAG, AI Agents | Udemy [Update 04/2026]
English | Size: 17.6 GB
Genre: eLearning[/center]
Build production-ready Generative AI applications with Spring AI - LLM, RAG, AI agents, Chat memory, MCP, Observability
What you'll learn
Learn and implement GenAI applications using Java and Spring AI
Learn how to call remote Large Language Models using Open AI, Google Gemini and Hugging Face APIs
Learn how to call local Large Language Models using Ollama and Docker Model Runner
Learn and implement Spring AI advanced concepts: Streaming, Structured output, Chat options, Advisors, Prompt templates
Learn Prompt engineering best practices including zero/one/few shot, CoT, changing creativity(temperature), controlled variability(top-p,) limiting tokens
Learn Prompt hacking techniques and their mitigation strategies: Prompt injection, Jailbreaking, Prompt leaking and Advanced hacking techniques
Understand GenAI and LLM fundamentals: Tokenizers, Embeddings, Positional encoding, Transformer architecture, Token prediction and Softmax formula
Understand chat memory and implement with multiple backends using Spring AI: In-memory and Jdbc for short-term memory, Vector store for long-term memory
Learn and implement multimodality using text, image and sound conversion use cases
Understand LLM limitations and their possible mitigations
Learn and implement advanced RAG systems
Learn and implement AI Agent systems with autonomous and chained workflow agentic systems
Understand MCP (Model Context Protocol) and implement MCP server and MCP client applications using Spring AI
Understand and apply Observability to RAG and AI Agent systems using Spring Observability
Want to build real-world Generative AI (GenAI) applications with Java, Spring Boot, Spring AI, RAG and AI Agents-not just experiment with prompts?
This course will take you from fundamentals to production-ready AI systems, including RAG pipelines, AI agents, tool calling, chat memory, MCP, observability, prompt engineering, and prompt hacking.
Hi there! My name is Ali Gelenler. I'm here to help you learn GenAI using Java and Spring AI from fundamentals to real-world production ready AI architectures and systems with a practical approach.
In this course, you will focus on creating AI applications to go beyond AI generated code and implement over 20 use cases using Java and Spring AI together with various AI providers and models, such as Open AI, Google Gemini Vertex AI, Hugging Face, Ollama and Docker Model Runner. You will build AI applications and AI systems using LLMs (Large Language Models), integrate vector databases and embeddings, and design scalable backend architectures for Generative AI.
You will learn:
Building end-to-end GenAI systems in Java and Spring AI with advanced Spring AI concepts
Designing RAG pipelines with vector databases, embeddings, similarity search and semantic search using advanced ingestion and retrieval strategies such as query transformer, query expander, pre/post processors, re-ranker, metadata filtering and dynamic resource updates
Creating AI agents with tool/function calling using autonomous and chained workflow agentic systems
Implementing chat memory and long-term context with in-memory, jdbc and vector store backends using Spring AI advisors
Applying prompt engineering best practices and defend against prompt hacking techniques including prompt injection, jailbreaking and prompt leaking attacks
Using MCP (Model Context Protocol) for distributed AI systems, creating MCP Server and MCP client using Spring AI
Adding Observability (logs, traces, metrics) to AI applications
Learning Gen AI and LLM Fundamentals with Tokenizers, Embeddings, Positional encoding, Transformer architecture, Token prediction and Softmax formula
Mapping the Gen AI and LLM Fundamentals into practical solutions
Understanding LLM limitations and possible mitigations
You will implement 20+ real-world use cases, including:
AI-powered assistants: Summarizer, Java Doc generator, Programming helper, Email drafter, Post generator
Document Q&A systems with advanced RAG pipelines
Security review from architectural diagram AI agent system with multiple tools including Remote Mcp Server tool, Web tool, RAG tool and Diagram extract tool, implementing both autonomous and chained workflow agent systems
Multimodal applications including Image-to-Text, Text-to-Image, Speech-to-Text and Text-to-Speech use cases
Order status helper with advanced chat memory strategies
Production-ready AI systems with monitoring and tracing
Technologies & tools you will use:
Java & Spring AI
Advanced Spring AI concepts: Streaming, Structured output, Chat options, Advisors, Prompt templates
OpenAI, Google Gemini (Vertex AI), Hugging Face APIs
Ollama & Docker Model Runner for local LLMs
Vector databases using PgVector
MCP (Model Context Protocol) with MCP Server and MCP Client implementations
Observability tools (Grafana, Prometheus, Otlp, Tempo, Jaeger, Loki and Promtail)
This is a practical and production-oriented course. You will not just generate code using AI tools-you will learn how to:
Design systems
Handle real-world limitations
Build scalable and maintainable AI applications
For more detailed information on the progress of this course, you can check the introductory video and free lessons, and if you decide to enroll in this course, you are always welcome to ask and discuss the concepts and implementation details on Q/A and messages sections. I will guide you from start to finish to help you successfully complete the course and gain as much knowledge and experience as possible from this course.
Support & updates
You can ask questions anytime in Q&A
The course will be continuously updated as Spring AI evolves
You'll get guidance to fully understand and apply concepts
Remember! There is a 30-day full money-back guarantee for this course! So you can safely press the 'Buy this course' button with zero risk and join this learning journey with me.
Who this course is for:
Java / Spring Boot developers who want to integrate GenAI into real-world applications
Backend engineers looking to build production-ready AI applications with RAG pipelines and AI agent systems
Software engineers who want to move beyond AI-generated code and understand how GenAI systems actually work
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