{"id":14688,"date":"2026-08-10T11:17:26","date_gmt":"2026-08-10T11:17:26","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-complete-genai-with-java-spring-ai-llms-rag-ai-agents-2026-5\/"},"modified":"2026-08-10T11:17:26","modified_gmt":"2026-08-10T11:17:26","slug":"udemy-complete-genai-with-java-spring-ai-llms-rag-ai-agents-2026-5","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-complete-genai-with-java-spring-ai-llms-rag-ai-agents-2026-5\/","title":{"rendered":"Udemy \u2013 Complete GenAI with Java & Spring AI: LLMs, RAG, AI Agents 2026-5"},"content":{"rendered":"
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Descriptions<\/h2>\n

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.<\/p>\n

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.<\/p>\n

What you\u2019ll learn<\/h3>\n