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 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.
