Spring AI 2.0 - From Java Legacy to AI Native

Mastering Spring AI 2.0 & GenAI: Advisors, Memory Management, RAG, Vector Databases, Tool Calling, and MCP
What you'll learn

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Master Spring AI: Seamlessly connect your Spring Boot apps to top LLMs like ChatGPT, Claude, and Gemini using clean, familiar Java code.
Vector DBs & RAG: Easily store and query enterprise data using advanced vector databases like Pinecone, Redis, Postgres, and MongoDB.
Advanced AI Patterns: Implement Chat Memory, Function/Tool Calling, Advisor APIs, and the cutting-edge Model Context Protocol (MCP).
Production Ready: Build secure, reliable AI features with built-in Observability and Guardrails without learning complex new SDKs.
Requirements

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Basic knowledge of Java and the Spring Boot framework is required.
A computer capable of running a Java IDE (IntelliJ).
Ablitiy to A small budget to purchase API keys (e.g., Gemini) to follow along.
Description

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AI Disclosure:This course contains the use of artificial intelligence. Transform into a Senior AI Engineer with the Most Modern Tech Stack in the Java Ecosystem.
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

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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.
By the end of the course, you won't just be a developer writing traditional Java code-you will be an AI engineer who knows how to design and build complex, secure, autonomous, and cost-effective intelligent applications.
Stop watching the AI revolution from the sidelines. Upgrade your Java stack, master Spring AI, and start building the future of enterprise software today!
Who this course is for

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Java & Spring Boot Developers looking to easily integrate AI into their existing backend applications.
Backend Engineers who want to leverage LLMs like ChatGPT, Claude, and Gemini with Java Tools
Software Architects interested in modern AI architectures like Vector DBs, RAG, and the MCP protocol.
Tech Professionals wanting to upgrade their skills and stay highly competitive in the GenAI market.
Homepage

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