Building AI Systems with Spring AI
Description
Building AI Systems with Spring AI” is a comprehensive course that teaches developers how to design and implement production-ready AI-powered applications using the Spring AI framework. The course covers the full spectrum of modern AI development, from understanding core architectural concepts and prompt engineering to building conversational agents with memory, integrating retrieval-augmented generation (RAG) pipelines, leveraging tool calling and MCP, and deploying both cloud-based and offline language models.
🕒 Duration: 8 hours
👥 Target Audience:
- Roles: Backend Engineers, Software Architects, Technical Leads, Solution Architects
- Seniority: Mid- to senior-level professionals
Webinar Content
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Module 1: Building AI Systems with Spring AI
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Spring AI Core Concepts |
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| Prompt Engineering |
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| Conversational AI & Memory |
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| RAG Foundations & Basic Monitoring |
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| Tool Calling & MCP |
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| Offline & Hybrid LLM Deployment |
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Learning Objectives:
After attending this webinar participants will be able to:
- Architect and build AI-powered services using Spring AI within a familiar Java/Spring ecosystem
- Write effective prompts using templates, few-shot examples, and structured output techniques
- Design stateful conversational applications with appropriate memory strategies
- Implement RAG pipelines to ground LLM responses in real data and reduce hallucinations
- Integrate external tools and MCP servers into multi-step AI workflows
- Deploy and switch between cloud-hosted and locally running language models using Docker
Prerequisite Knowledge
- Solid understanding of Java and the Spring Framework (Spring Boot experience is strongly recommended)
- Familiarity with REST APIs and basic software architecture patterns
- Basic understanding of what Large Language Models (LLMs) are and how they are used
- Experience with Docker is helpful, particularly for the offline deployment module

