AI Engineering: Building AI Applications with Spring AI

About this course

You will develop the same Spring AI application throughout the course. You will start with a new Spring project, then move through model response evaluation, document-based applications, tool calling, MCP, and agentic workflows.

Before you start

Basic experience with Java and Spring Boot, plus foundational knowledge of REST APIs and databases.

What you will learn

Your first steps with Spring AI
Evaluating model responses
Prompts, templates, and response formats
Document-grounded answers with RAG
Conversation memory and context
Tool calling and taking action
MCP servers and clients
Working with audio and images
Metrics and prompt tracing
Security for AI applications
AI applications beyond chat
Agentic workflows and Embabel

Course curriculum

12 modules 36 lessons

1.1What is Spring AI and what problems does it solve?Read lesson
1.2Creating a new Spring projectRead lesson
1.3Building your first Spring AI applicationRead lesson

About this course

Before you start

What you will learn

Course curriculum

1Your first steps with Spring AIGet familiar with Spring AI, create a new Spring project, and build the foundation of the application you will develop throughout the course.3 lessons

2Evaluating model responsesUse Spring AI evaluators to check the quality, factual accuracy, and context grounding of large language model (LLM) responses.3 lessons

3Prompts, templates, and response formatsLearn different ways to submit prompts, define response formats, and add context that prepares your application for RAG.3 lessons

4Document-grounded answers with RAGBuild retrieval-augmented generation (RAG) with Spring AI advisors. Use documents outside the model's training data as sources for answers.3 lessons

5Conversation memory and contextKeep earlier messages in the conversation context and pass them into later prompts to build a continuous dialogue.3 lessons

6Tool calling and taking actionGive the model tools to retrieve data from APIs and databases and perform operations in response to a prompt.3 lessons

7MCP servers and clientsCollect tools, resources, and prompt templates in a Model Context Protocol (MCP) server. Build an MCP client in your application to use that server.3 lessons

8Working with audio and imagesUse compatible models to turn audio into text and text into speech. Ask questions about images and create images from text prompts.3 lessons

9Metrics and prompt tracingUse Spring Boot Actuator and Micrometer to observe application metrics and trace prompts as they move through Spring AI.3 lessons

10Security for AI applicationsUse Spring Security to enforce permissions. Restrict tool calls and the documents used for RAG context to authorized users.3 lessons

11AI applications beyond chatUse Spring AI to summarize and translate text and analyze the sentiment expressed in it.3 lessons

12Agentic workflows and EmbabelBuild common agentic workflow patterns with Spring AI. Explore Embabel, built on Spring AI, and its automatic planning toward a goal.3 lessons