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

Name: Build AI Apps with Spring AI, OpenAI, Ollama & SpringBoot
Rating: 4.5 (246 reviews)

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

Bestseller

Highest Rated

Created byPragmatic Code School

Last updated 2/2026

English

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.

Course content

17 sections • 81 lectures • 9h 24m total length

Course Introduction4:30
Begin your journey with Spring AI by exploring the course structure, key learning objectives, and the practical applications you'll build—from prompt engineering to Retrieval-Augmented Generation (RAG). This overview sets the stage for integrating large language models into real-world Spring Boot applications.
Pre-requestites1:37
In this lecture, I will cover the prerequisites that are needed for this course.

The World Before LLMs: A Glimpse into the Past"?8:53
Explore the pre-LLM era, How we interact with computers for solutions ? How its changed since the release of ChatGPT.
Large Language Models and its Evolution8:42
Trace the evolution of Large Language Models, from early iterations to the cutting-edge architectures shaping today's AI landscape.
Advantages, Challenges with LLMs5:11
Examine the benefits, hurdles, and diverse applications of LLMs, showcasing their impact across various industries and use cases.
Sign up for a ChatGPT Account and Start Exploring11:08
Learn how to sign up for ChatGPT, navigate its features, and begin experimenting with this powerful AI tool in practical scenarios.

Introduction to Spring AI7:21
Learn the basics of Spring AI and its integration with OpenAI to build intelligent applications seamlessly.
Setup OpenAI Account & Open AI Playground7:42
Set up your OpenAI account and explore the Playground to experiment with generative AI capabilities.
Set Up Java using SDKMan6:50
Install and configure Java efficiently using SDKMan to prepare your development environment.
Set up the Base Project using Gradle and IntelliJ5:21
Create and configure your Java project with Gradle and IntelliJ for streamlined development.
Lets Chat with OpenAI Platform using Spring "ChatClient"10:48
Build a chat-based AI application using Spring's ChatClient to interact with OpenAI's platform.
Setting Up Insomnia to Test the Chats Endpoint7:10
Use Insomnia to test and debug your AI chat endpoints effectively.
How does the ChatClient Work under the hood ? - AutoConfiguration & API call to8:29
Dive into the inner workings of ChatClient's auto-configuration to understand its architecture and functionality.
Prompt, Tokens and Tokenization - What are they ?5:43
Understand the key concepts of prompts, tokens, and tokenization and how they impact AI responses.
OpenAI Request Parameters - temperature5:20
Explore the temperature parameter to control the creativity of OpenAI's generated responses.
OpenAI Request Parameters - max_tokens3:10
Learn how to manage response length effectively using the max_tokens parameter.
Understanding System, Assistant, and User Messages in OpenAI6:11
Learn the roles of system, assistant, and user messages in structuring AI interactions effectively.
System, Assistant, and User Messages in Action2:25
See practical examples of system, assistant, and user messages in action to design structured conversations
Streaming OpenAI Responses10:27
Implement real-time streaming to display OpenAI responses incrementally as they are generated.

Building prompts using the Prompt and ChatClient11:03
Learn how to construct effective prompts using the Prompt and ChatClient to streamline AI interactions
Enhancing Prompt Management with StringTemplate3:13
Learn to design and implement a robust endpoint that leverages string templates to dynamically generate and customize code for various use cases.
Using PromptTemplate: A Simplified Way to Pass Messages to AI6:51
Learn how to effectively utilize PromptTemplate to structure and streamline the exchange of messages in AI applications
Passing Dynamic Values to Prompts - Alternative Approach3:39
Learn how to use StringTemplate to dynamically inject values into prompts, making them more flexible, reusable, and context-aware.

A Quick Introduction to Spring Advisors3:40
Understand the fundamentals of Spring AI Advisors and how they can be used to modify AI interactions.
Spring Advisor in Action - SimpleLoggerAdvisor3:56
Explore the SimpleLoggerAdvisor to log requests and responses, aiding in debugging and monitoring AI interactions.
Let's build our own Build Custom Advisor7:57
Learn how to create a custom advisor to tailor AI interactions to specific application needs.

Prompt Engineering & PromptTemplate4:09
This lecture teaches how to create clear, structured prompts that lead to accurate and meaningful AI outputs.
Prompt Engineering in Action - Lets explore the Travel Plan Prompt10:55
Dive into a practical example by creating a travel plan prompt, showcasing prompt engineering techniques in a real-world scenario.
Understanding Prompt Injection and How to Mitigate it?9:47
Explore the concept of prompt injection, its potential risks and the techniques to mitigate prompt injection..
Zero Shot Prompting5:48
This lecture explains zero-shot and few-shot prompting techniques to improve AI performance with minimal examples.
Few Shot Prompting5:06
This lecture explains zero-shot and few-shot prompting techniques to improve AI performance with minimal examples.
Chain of Thought Prompting9:37
Learners will explore how Chain of Thought Prompting helps guide AI through logical, step-by-step problem-solving.
Mastering Multi-Step Prompts8:41
Learn how to craft and utilize multi-step prompts to guide LLMs through complex tasks, achieving more detailed and accurate responses.

Introduction to Structured Outputs in LLM6:26
Understand how LLMs generate structured data and why it's crucial for AI-driven applications.
Structured outputs using Prompt Engineering7:22
Explore how the entity() function and BeanOutputConverter work together to produce structured outputs seamlessly in Spring AI.
Structured outputs with Few Shot Examples using Prompt Engineering4:35
Learn how few-shot examples enhance prompt engineering to produce more accurate structured outputs.
Structured Outputs using entity() function - Using BeanOutputConverter5:43
Explore how the entity() function and BeanOutputConverter work together to produce structured outputs seamlessly in Spring AI.
How Structured Outputs work in SpringAI - Under the Hood?4:00
Get an inside look at how Spring AI handles structured outputs behind the scenes, from prompt to parsed response.
Using ParameterizedTypeReference for Structured Outputs - List Collection Types7:27
Learn how to use ParameterizedTypeReference to handle structured outputs involving collection types like lists and maps effectively.
Generate Structured Outputs of type "Map" Collection Type5:07
Learn how to generate and parse structured outputs of type Map using Spring AI, ideal for handling key-value data responses.

Tool Calling - What & Why ?8:47
Understand the fundamentals of function calling and why it's essential for AI integration. Discover how OpenAI uses function calling to access and manipulate external data.
Tool Calling with OpenAI: Accessing System Time12:33
Learn how to use OpenAI's function calling to fetch system details like name and time. This lecture demonstrates how AI can interact with your device for dynamic information retrieval.
How Tool Calling works under the hood" in OpenAI and Spring AI?10:01
Go behind the scenes to understand how tool calling operates internally—covering request preparation, function resolution, and response parsing in Spring AI.
Tool Calling in Action: Accessing Live Currency Exchange Rates20:01
See tool calling in action as you integrate with OpenExchangeRates to fetch real-time currency exchange rates using Spring AI.
Tool Calling in Action using Functions: Accessing Live Weather Data13:56
Learn how to fetch real-time weather data using function-based tool calling with OpenAI, demonstrating practical integration for dynamic responses
Tool Calling in Action using Spring Beans: Accessing Live Weather Data4:17
Explore how Spring Beans can be used with tool calling to retrieve live weather data in a modular and scalable way.
Structured Outputs using Tool Calling with "ReturnDirect"2:08
Discover how to generate structured outputs using tool calling, enabling reliable parsing of data from external APIs.
Adding Metadata to the Tool Calling using ToolContext3:27
Learn how to customize tool calling behavior using ToolContext and the returnDirect flag to control response handling and execution flow.

Requirements

Basic knowledge of Java and Spring Boot will help, but the course covers concepts from scratch for new learners.
No prior experience with AI, OpenAI, or LLMs is required—everything is explained in a simple, hands-on way.
Familiarity with REST APIs and JSON can be helpful, but not mandatory to follow along with the projects.
Curiosity and willingness to experiment with code are the most important things you’ll need for this course.

Description

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.

Who this course is for:

Java developers curious about building AI-powered apps using Spring AI and modern generative AI techniques.
Beginners looking to get started with Spring AI, LLMs, and practical generative AI use cases without needing prior AI experience.
Backend engineers and full-stack developers wanting to integrate OpenAI’s LLMs into real-world Spring Boot applications.
Tech enthusiasts and students eager to explore hands-on projects involving chatbots, RAG, tool calling, and multimodality.
Professionals interested in learning how to build production-ready AI applications with Spring Boot and OpenAI.
Anyone looking to future-proof their Java skills by diving into the world of generative AI and building practical AI tools.

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

What you'll learn

Explore related topics

Course content

Introduction2 lectures • 6min

Course Slides and Source Code2 lectures • 1min

Introduction to Large Language Models (LLMs), OpenAI & ChatGPT [ Theory ]4 lectures • 34min

Getting Started with Spring AI and OpenAI API13 lectures • 1hr 27min

Global ErrorHandler to deal with Exception/Errors using Github Copilot2 lectures • 15min

Streamlining Message Passing to LLMs using StringTemplates & PromptTemplates4 lectures • 25min

Spring AI Advisors: Enhancing AI Interactions3 lectures • 16min

Prompt Engineering7 lectures • 54min

Generating Structured Data with OpenAI & Spring AI7 lectures • 41min

Augmenting LLMs using Tool Calling using Spring AI8 lectures • 1hr 15min

Requirements

Description

Who this course is for: