Udemy – Spring AI 2.0 – From Java Legacy to AI Native

Generative AI is no longer just a trend—it is shifting the foundation of enterprise software architecture. Traditional Java applications are rapidly evolving into intelligent, AI-native systems capable of autonomous reasoning, real-time context retrieval, and complex task execution. This course is engineered specifically for Java developers, backend engineers, and solution architects who want to step into the future of software development. You won’t just learn how to connect an API to a chatbot; you will master the art of designing and building complex, autonomous, and cost-effective enterprise AI applications using the latest standards: Spring AI 2.0.0

What You Will Learn in the Course:

• The Shift to AI-Native: Understanding the fundamental architectural transition required to upgrade traditional Spring architectures to support heavy generative capabilities.
• Retrieval-Augmented Generation (RAG): Learn how to securely connect your private enterprise data to Large Language Models (LLMs) such as OpenAI and Gemini using high-performance vector databases.
• Autonomous AI Agents and Tool Calling: Moving beyond passive chatbots. Learn how to build proactive AI agents capable of automatically selecting and executing internal Java functions (Tool Calling) to interact with your databases, external APIs, and business logic in real time.
• Model Context Protocol (MCP): Master the open protocol that has become the industry standard for AI data integration. Learn how to implement MCP to grant your language models unified and secure access to data sources, enterprise systems, and developer tools without needing to rewrite complex middleware.
• Prompt Engineering and Structured Outputs: Master the art of programmatically writing precise prompts and forcing language models to return well-defined, type-safe Java objects instead of raw, unpredictable text.
• Token Metrics and Cost Optimization: Take a deep dive into how tokens work behind the scenes, how to build custom wrappers for language models, and how to programmatically track, monitor, and reduce API costs to save significant amounts of money.