Descriptions
Complete GenAI with Java & Spring AI: LLMs, RAG, AI Agents, This course takes you from fundamentals to production-ready Generative AI (GenAI) systems using Java, Spring Boot, and Spring AI. You will learn to build real-world applications, not just experiment with prompts, covering RAG pipelines, AI agents, tool calling, chat memory, MCP, observability, prompt engineering, and prompt hacking. The curriculum focuses on creating AI applications by implementing over 20 use cases with various AI providers like OpenAI, Google Gemini, Hugging Face, and local models via Ollama. You will build AI systems using LLMs, integrate vector databases and embeddings, and design scalable backend architectures for GenAI, including production-ready AI agents with tool calling, workflow design, chaining, and human-in-the-loop patterns.
Key learning areas include building end-to-end GenAI systems, designing advanced RAG pipelines with sophisticated ingestion and retrieval strategies, and creating AI agents with autonomous and chained workflows. You will also implement chat memory, apply prompt engineering best practices, defend against prompt hacking, use the Model Context Protocol (MCP) for distributed systems, and add observability (logs, traces, metrics) to your applications. The course covers GenAI and LLM fundamentals, such as tokenizers, embeddings, and transformer architecture, and maps these concepts to practical solutions while addressing LLM limitations and their mitigations.
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
- Learn and implement Human-in-the-loop patter in AI Agent 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
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
Specificatoin of Complete GenAI with Java & Spring AI: LLMs, RAG, AI Agents
- Publisher : Udemy
- Teacher : Ali Gelenler , EA Algorithm
- Language : English
- Level : All Levels
- Number of Course : 135
- Duration : 25 hours and 51 minutes
Content of Complete GenAI with Java & Spring AI: LLMs, RAG, AI Agents
Requirements
- Basic to intermediate knowledge of Java
- Familiarity with Spring Boot
- Experience with backend development concepts
- Basic familiarity with AI/LLM concepts
Pictures
Sample Clip
Installation Guide
Extract the files and watch with your favorite player
Subtitle : English
Quality: 720
Download Links
Password file(s): www.downloadly.ir
File size
10.25 GB
