Complete GenAI with Java & Spring AI: LLMs, RAG, AI Agents

<div>Want to build real-world Generative AI (GenAI) applications with Java, Spring Boot, Spring AI, RAG and AI Agents—not just experiment with prompts?</div><div><br></div><div>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.</div><div><br></div><div>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.</div><div><br></div><div>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.</div><div><br></div><div>You will learn:</div><div><ul><li><span style="font-size: 1rem;">Building end-to-end GenAI systems in Java and Spring AI with advanced Spring AI concepts</span></li><li><span style="font-size: 1rem;">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</span></li><li><span style="font-size: 1rem;">Creating AI agents with tool/function calling using autonomous and chained workflow agentic systems</span></li><li><span style="font-size: 1rem;">Implementing chat memory and long-term context with in-memory, jdbc and vector store backends using Spring AI advisors</span></li><li><span style="font-size: 1rem;">Applying prompt engineering best practices and defend against prompt hacking techniques including prompt injection, jailbreaking and prompt leaking attacks</span></li><li><span style="font-size: 1rem;">Using MCP (Model Context Protocol) for distributed AI systems, creating MCP Server and MCP client using Spring AI</span></li><li><span style="font-size: 1rem;">Adding Observability (logs, traces, metrics) to AI applications</span></li><li><span style="font-size: 1rem;">Learning Gen AI and LLM Fundamentals with Tokenizers, Embeddings, Positional encoding, Transformer architecture, Token prediction and Softmax formula</span></li><li><span style="font-size: 1rem;">Mapping the Gen AI and LLM Fundamentals into practical solutions</span></li><li><span style="font-size: 1rem;">Understanding LLM limitations and possible mitigations</span></li></ul></div><div><span style="font-size: 1rem;">You will implement 20+ real-world use cases, including:</span></div><div><ul><li><span style="font-size: 1rem;">AI-powered assistants: Summarizer, Java Doc generator, Programming helper, Email drafter, Post generator</span></li><li><span style="font-size: 1rem;">Document Q&amp;A systems with advanced RAG pipelines</span></li><li><span style="font-size: 1rem;">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</span></li><li><span style="font-size: 1rem;">Multimodal applications including Image-to-Text, Text-to-Image, Speech-to-Text and Text-to-Speech use cases</span></li><li><span style="font-size: 1rem;">Order status helper with advanced chat memory strategies</span></li><li><span style="font-size: 1rem;">Production-ready AI systems with monitoring and tracing</span></li></ul></div><div><span style="font-size: 1rem;">Technologies &amp; tools you will use:</span></div><div><ul><li><span style="font-size: 1rem;">Java &amp; Spring AI</span></li><li><span style="font-size: 1rem;">Advanced Spring AI concepts: Streaming, Structured output, Chat options, Advisors, Prompt templates</span></li><li><span style="font-size: 1rem;">OpenAI, Google Gemini (Vertex AI), Hugging Face APIs</span></li><li><span style="font-size: 1rem;">Ollama &amp; Docker Model Runner for local LLMs</span></li><li><span style="font-size: 1rem;">Vector databases using PgVector</span></li><li><span style="font-size: 1rem;">MCP (Model Context Protocol) with MCP Server and MCP Client implementations</span></li><li><span style="font-size: 1rem;">Observability tools (Grafana, Prometheus, Otlp, Tempo, Jaeger, Loki and Promtail)</span></li></ul></div><div><span style="font-size: 1rem;">This is a practical and production-oriented course. You will not just generate code using AI tools—you will learn how to:</span></div><div><ul><li><span style="font-size: 1rem;">Design systems</span></li><li><span style="font-size: 1rem;">Handle real-world limitations</span></li><li><span style="font-size: 1rem;">Build scalable and maintainable AI applications</span></li></ul></div><div><span style="font-size: 1rem;">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.</span></div><div><br></div><div>Support &amp; updates</div><div><ul><li><span style="font-size: 1rem;">You can ask questions anytime in Q&amp;A</span></li><li><span style="font-size: 1rem;">The course will be continuously updated as Spring AI evolves</span></li><li><span style="font-size: 1rem;">You’ll get guidance to fully understand and apply concepts</span></li></ul></div><div><span style="font-size: 1rem;">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.</span></div>

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