Udemy

Build AI Apps with Spring AI, OpenAI, Ollama & SpringBoot

Enroll Now
  • 1,164 Students
  • Updated 11/2025
4.4
(100 Ratings)
CTgoodjobs selects quality courses to enhance professionals' competitiveness. By purchasing courses through links on our site, we may receive an affiliate commission.

Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
9 Hour(s) 24 Minute(s)
Language
English
Taught by
Pragmatic Code School
Rating
4.4
(100 Ratings)

Course Overview

Build AI Apps with Spring AI, OpenAI, Ollama & SpringBoot

Learn chat with LLMs, Retrieval-Augmented Generation, tool calling, and multimodal AI using Spring AI.

Course Description

Unlock the power of Generative AI within your Java applications using Spring AI, OpenAI, and Ollama!
In this hands-on course, you’ll learn how to build intelligent, scalable AI-driven applications using the robust Spring Boot ecosystem. From crafting prompts to building full RAG-based systems, you’ll gain practical skills to integrate LLMs into real-world projects.

Here’s a breakdown of what you’ll learn in each section:

Course Introduction & Setup

  • Understand the course structure, prerequisites, and how to set up your Java and Spring AI environment.

Introduction to Large Language Models (LLMs), OpenAI & ChatGPT

  • Learn the basics of LLMs, their evolution, applications, and how OpenAI’s ChatGPT fits into modern AI workflows.

Getting Started with Spring AI and OpenAI API

  • Configure your project and IDE, create your first chat-based app using ChatClient, and understand prompts, tokens, and OpenAI request parameters.

Working with Chat Models and OpenAIChatModel

  • Customize LLM responses using ChatOptions, enable streaming, and build responsive AI chat applications.

Prompt Engineering with Spring AI

  • Master prompt engineering techniques like zero-shot, few-shot, chain-of-thought, and multi-step prompting to guide AI outputs effectively.

Generating Structured Data with Spring AI

  • Learn to create structured outputs using prompt templates and Spring’s converters, including lists, maps, and entity objects.

Tool Calling (Function Calling) with Spring AI

  • Integrate external systems into your AI apps with OpenAI’s tool calling—fetch live data like weather, currency rates, and more.

Building RAG Applications (Retrieval-Augmented Generation)

  • Build an end-to-end RAG-powered Q&A system using PgVector, document chunking, indexing, and semantic retrieval.

Document Ingestion Strategies

  • Explore how to ingest and chunk various document types including PDFs, Word files, and plain text using different readers and splitters.

Exploring Multimodality: Vision Capabilities

  • Leverage OpenAI’s image models to generate, analyze, and process images including real-world examples like invoice parsing.

Exploring Multimodality: Audio Capabilities

  • Convert text to realistic voice using TTS, and transcribe or translate speech to text using the Whisper API.

Building Local AI Apps with Spring AI and Ollama

  • Run LLMs locally using Ollama, integrate it with Spring AI, and build applications without relying on external APIs.

By the end of this course, you’ll be equipped to build full-stack AI-powered applications using Java and Spring Boot, with integrations that span cloud-based models, local deployments, vision, audio, and retrieval-augmented techniques.

You’ll walk away with the confidence and experience to bring Generative AI into production-ready Java applications.

Course Content

  • 10 section(s)
  • 81 lecture(s)
  • Section 1 Introduction
  • Section 2 Course Slides and Source Code
  • Section 3 Introduction to Large Language Models (LLMs), OpenAI & ChatGPT [ Theory ]
  • Section 4 Getting Started with Spring AI and OpenAI API
  • Section 5 Global ErrorHandler to deal with Exception/Errors using Github Copilot
  • Section 6 Streamlining Message Passing to LLMs using StringTemplates & PromptTemplates
  • Section 7 Spring AI Advisors: Enhancing AI Interactions
  • Section 8 Prompt Engineering
  • Section 9 Generating Structured Data with OpenAI & Spring AI
  • Section 10 Augmenting LLMs using Tool Calling using Spring AI

What You’ll Learn

  • Learn to integrate Large Language Models (LLMs) with Spring AI to build interactive chat applications using Java and Spring Boot.
  • Understand and implement Retrieval-Augmented Generation (RAG) to enhance LLM responses using custom and external knowledge sources.
  • Master tool calling with Spring AI to enable your LLM apps to take real-world actions like querying APIs or performing tasks.
  • Build multimodal applications that work with text, images, and audio by leveraging the latest OpenAI capabilities in Spring AI.
  • Learn how to configure and work with Spring AI clients, including prompt templates, message history, and streaming responses.
  • Build and deploy multiple real-world projects that cover chatbots, search assistants, and AI-powered tools using Spring AI.


Reviews

  • S
    Sagaya Kandasamy
    5.0

    very good

  • V
    Vikas Kumar1
    5.0

    good practice and learning

  • C
    Can C
    4.0

    I was a beginner with the OpenAI API, and I learned a lot from this course about the fundamentals of the API and the key concepts. I also learned how to use Spring AI to access what OpenAI offers. The only part missing from the course was how to manage message history when chatting with OpenAI. I believe this wasn't covered (if I'm not wrong). Other than that, the curriculum for Spring AI fundamentals was excellent, clear videos, explanations, and helpful code examples. I would definitely recommend this course.

  • M
    Madhankumar.BN
    5.0

    Good

Start FollowingSee all

We use cookies to enhance your experience on our website. Please read and confirm your agreement to our Privacy Policy and Terms and Conditions before continue to browse our website.

Read and Agreed