Name: Spring AI: Build Java AI Apps, Chatbots & RAG Systems (2026)
Rating: 4.4 (22 reviews)

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Created byRishabh Kumar Nigam

Last updated 1/2026

English

What you'll learn

Master LLM fundamentals—tokens, prompts, context windows—to build a strong foundation for all Spring AI features.
Build production-ready AI applications using Spring AI, Java, and modern LLMs across text, image, audio, and multimodal capabilities.
Integrate multiple AI providers—OpenAI, Mistral, Stability AI, Ollama—without vendor lock-in or complex SDK implementations.
Run AI models locally with Ollama and Docker for free, offline, secure, and customizable AI development workflows.
Build real-time streaming chat experiences using Spring WebFlux and locally running LLMs for faster and more interactive responses.
Create intelligent AI agents with Spring AI Tool Calling to execute business logic, fetch external data, and automate workflows securely.
Implement complete RAG pipelines using embeddings, semantic search, and cosine similarity to generate accurate, context-rich answers.
Design scalable knowledge bases with document chunking, metadata enrichment, embedding generation, and PgVector vector storage.
Develop an end-to-end HR Assistant chatbot with admin APIs, chat memory, tool-calling agents, and a fully integrated React UI.
Apply enterprise-grade engineering patterns to architect, test, and deploy real AI features inside production Java applications.

Course content

8 sections • 59 lectures • 6h 58m total length

Meet Your Instructor2:57
Get introduced to your instructor’s background and understand how the course will guide you into practical AI development with Java and Spring.
How to Succeed in This Course5:05
Learn proven strategies to navigate the course effectively and build real-world AI skills step by step.
Why AI + Java + Spring Right Now?3:00
Discover why combining AI with Java and Spring is in high demand and how it positions you for modern software roles.
How LLMs Think: Tokens, Prompts, and Context Windows5:49
Understand the core mechanics of LLMs—tokens, prompting, and context—which shape all AI-driven applications.

Module Introduction and Walkthrough1:43
Get an overview of the module and understand how you’ll build a complete Spring AI application from scratch.
Creating Your First Spring AI Project from Scratch4:59
Learn how to generate and configure a Spring Boot project prepared for AI integrations and model interactions.
Signing Up & Getting Free OpenAI API Keys1:58
See how to obtain and manage OpenAI API keys required to authenticate your Spring AI application.
Configure API Keys & Run Your First Spring AI App1:52
Discover how to securely configure API credentials and run a functional Spring AI application.
Exposing a Text Generation API with Spring AI5:58
Build your first REST endpoint that uses a Spring AI model to generate dynamic text responses.
Testing Your Spring AI Text Generation Endpoint1:09
Validate your AI-powered endpoint using tools like Postman to ensure correct request-response behavior.
Spring AI Internals: How Model Calls Work3:26
Explore how Spring AI handles model calls under the hood using auto-configuration and the ChatModel abstraction.
Spring AI Fundamentals & Project Setup Quiz
Module Recap0:33
Review the key concepts learned, reinforcing how a complete Spring AI application is built end-to-end.

Introduction to Spring AI Model Types4:02
Understand the different AI model categories supported by Spring AI and how each one fits into real application workflows.
Image Generation with OpenAI12:42
Learn how to generate high-quality images using OpenAI’s image models through Spring AI’s unified API.
Free Image Generation with Stability AI11:17
Discover how to create AI images at zero cost using Stability AI and integrate them seamlessly with Spring AI.
Fine-Tuning Image Generation Model2:34
Explore techniques for improving image output quality by adjusting parameters and tuning generation settings.
Text-to-Speech with OpenAI9:51
Learn how to convert text into natural-sounding audio using OpenAI’s speech synthesis models in Spring AI.
Improve Speech Output Quality and Voice Settings1:20
See how to customize voices and refine audio quality using advanced text-to-speech configuration options.
Speech-to-Text with OpenAI7:14
Understand how to transcribe audio into text using OpenAI’s speech-to-text models integrated into Spring AI.
Spring AI AudioOptions Explained1:48
Get clarity on Spring AI’s AudioOptions and how they control speech generation and transcription behavior.
Content Moderation with OpenAI7:46
Learn to analyze and classify unsafe content using OpenAI’s moderation models within Spring AI.
Free Moderation with Mistral AI11:51
Discover how to implement content moderation without cost by using Mistral’s moderation models.
Moderation in Real Applications (Best Practices)1:55
Explore practical moderation strategies to keep AI-powered applications safe and compliant.
Vision Input for Chat Models (Multimodal AI)10:48
Learn how to send images to chat models and enable visual understanding using Spring AI’s multimodal features.
Audio Input for Chat Models (Multimodal AI)8:52
Understand how to provide audio as input to chat models for multimodal reasoning in Spring AI.
Tune Chat Responses for Better Accuracy3:39
See how to refine AI responses using temperature, max tokens, and other tuning parameters for better output quality.

Introduction: Running AI Models Locally3:32
Understand the benefits of running AI models locally and how Spring Boot integrates with on-device LLMs.
Installing and Setting Up Ollama5:40
Learn how to install Ollama and prepare your machine to run fast, local large language models.
Switching from OpenAI to Ollama for Text Generation10:51
See how to replace external APIs with local models using Spring AI’s interchangeable provider system.
Real-Time Streaming Responses with Spring WebFlux3:26
Discover how to deliver live AI responses using streaming capabilities powered by Spring WebFlux.
Using Ollama for Multimodal Image Understanding8:11
Running Any LLM Locally with Docker11:55
Explore how Docker enables running diverse LLMs locally without manual installation or complex setup.
Integrating Spring Boot with Docker-Hosted LLMs (Whisper)10:16
See how to connect Spring Boot to Docker-hosted AI services like Whisper for local audio processing.

Introduction to Tool Calling in Spring AI3:21
Understand how tool calling enables LLMs to trigger real actions and enrich your Spring AI applications.
Building Your First Information & Action Tools with Spring AI16:21
Learn to create custom tools that let AI fetch data or perform operations through your Spring Boot backend.
Weather Tool for Smarter User Suggestions14:17
See how to build a weather-powered AI tool that enhances responses with real-time contextual insights.
Understanding Internals of @Tool and @ToolParam in Spring AI3:48
Explore how Spring AI processes tool annotations and parameters to deliver structured model interactions.
User-Controlled AI Tool Execution14:18
Discover how to let users selectively approve or trigger AI tools for safer, more transparent automation.

Understanding Embeddings4:03
Understand how embeddings convert text into vectors that capture semantic meaning for AI search and retrieval.
Setting Up an Embedding Model and Generating Embeddings12:54
Learn how to configure an embedding model in Spring AI and generate vector representations from your data.
Introduction to RAG & Implementing Cosine Similarity10:17
Explore how RAG works and use cosine similarity to measure semantic closeness between queries and documents.
Custom RAG: Preparing and Embedding Your Data7:45
Discover how to clean, chunk, and embed your documents to build a foundation for a custom RAG system.
Custom RAG: Retrieving Relevant Chunks with Cosine Similarity5:16
Learn to perform semantic search by retrieving the most relevant document chunks using cosine similarity.
Custom RAG: Generating Answers from Retrieved Context8:07
See how to generate accurate AI responses by feeding retrieved context into your Spring AI model.

Vector Databases Explained & Project Setup6:21
Learn why vector databases matter for AI apps and set up your Spring Boot project for PgVector-powered RAG.
Setting Up PgVector: Embeddings, Metadata, and Document Storage14:43
See how to configure PgVector and store enriched document embeddings with metadata for structured retrieval.
Semantic Search with PgVector + Prompt Engineering11:46
Understand how to run semantic queries with PgVector and enhance responses using Spring AI’s RAG advisors.
Deleting PgVector Documents with Metadata Filters8:47
Learn how to remove document chunks safely using metadata-based filters for clean, controlled knowledge updates.

Building Admin APIs for the Knowledge Base12:48
Learn how to create admin APIs that upload, chunk, and store HR documents into your vector-backed knowledge base.
Testing the Admin Knowledge Base APIs9:45
Validate document ingestion and ensure your admin endpoints correctly manage stored HR policies.
Creating User APIs: Adding Chat Memory11:38
Discover how to store chat history and power personalized conversations using Spring AI’s memory system.
Creating User APIs: Conversation Management13:05
Learn to implement APIs that create, fetch, and manage user conversations across multiple chat sessions.
Building the Chat APIs5:33
Build the core chat endpoint that retrieves context and generates AI-powered HR responses using RAG.
Testing the Chat APIs4:22
Confirm your chatbot’s behavior by testing the end-to-end flow of prompts, retrieval, and model responses.
Connecting Frontend & Backend (CORS Setup)4:47
Configure CORS to securely connect your React frontend with your Spring Boot backend.
AI-Assisted UI Generation (React Frontend)9:41
Learn how to generate a polished React frontend using AI tools to accelerate UI development.
Running the Complete HR Chatbot Locally (Frontend + Backend)7:28
Run the full system locally and see your Spring AI backend and React frontend working together.
Customizing the HR Chatbot UI with Copilot6:42
Explore how GitHub Copilot enhances your UI with custom styling, layouts, and interactive features.
Project Wrap-Up & Next Steps3:05
Review the full project and learn practical next steps for deploying and extending your HR assistant.

Requirements

Basic Java knowledge (classes, methods, OOP fundamentals).
Light familiarity with Spring Boot — just enough to understand basic project structure; other things will be taught inside the course.
No AI/ML background required — all AI concepts are taught from scratch
No paid APIs needed — we use free options like Ollama and free-tier keys.

Description

The AI revolution is here, and enterprise systems are still powered by Java. Java developers need a modern, practical way to integrate LLMs without deep data science knowledge. This course is the direct answer, transforming you from a Spring Boot developer into a high-demand AI engineer.

We cut through the noise and show you exactly how to build robust, scalable AI features using the familiar patterns of the Spring ecosystem.

We move quickly from foundational concepts to hands-on, production-ready features.

Foundations (Module 1): Master the core mechanics of LLMs—tokens, prompts, and context windows—which are the building blocks of every AI application.
Core Integration (Module 2-3): Build your first Spring AI application from scratch. Go beyond text generation to integrate image generation , Text-to-Speech (TTS) , Speech-to-Text (STT) , and multimodal (vision/audio) capabilities. You'll implement moderation pipelines using both OpenAI and the free Mistral model.
The Power of Local AI (Module 4): Free yourself from cloud costs and latency. Learn how to install and use Ollama to run fast, local models like Gemma directly on your machine. We implement real-time streaming using Spring WebFlux and even integrate local Whisper Api via Docker.
Intelligent Agents (Module 5): Build AI agents that take actions. Master Tool Calling (Function Calling) to let the LLM securely trigger your Spring Boot business logic, fetch real-time data (like weather) , and orchestrate complex workflows.
RAG Mastery (Module 6-7): The most critical enterprise skill. We start by building a custom RAG pipeline from scratch using embeddings and cosine similarity. Then, we integrate fully with PgVector—the gold standard for RAG—to implement scalable semantic search, document ingestion (PDF chunking via Tika), and lifecycle management.
The Capstone Project (Module 8): Bring it all together by building a Full-Stack HR Assistant Chatbot. This project features:
- Admin APIs for knowledge base management.
- Spring AI Chat Memory for personalized conversations.
- A full conversation management API.
- A complete, AI-generated React Frontend.

By the end of this course, you will have the confidence and portfolio to build real, feature-rich, AI-powered applications that solve genuine business problems.

Who this course is for:

Java developers aiming to move into AI engineering with a practical, project-driven course instead of theory-heavy learning.
Students who know basic Java and want hands-on AI projects.
Engineers wanting to build enterprise-grade AI features like chatbots and RAG workflows.
Backend devs interested in vector databases, embeddings, and semantic search.
Teams exploring AI adoption using familiar Java tooling.
Developers wanting to run AI models locally and for free with Spring AI + Ollama.

What you'll learn

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Course content

Introduction & Foundations4 lectures • 17min

Getting Started with Your First Spring AI Project8 lectures • 22min

AI Model Integration with Spring AI (OpenAI, Stability, Mistral)14 lectures • 1hr 36min

Running AI Models Locally with Spring Boot7 lectures • 54min

Tool Calling in Spring AI5 lectures • 52min

Embeddings & Basic RAG6 lectures • 48min

Vector Databases & Spring AI RAG Integration4 lectures • 42min

Building the HR Assistant Chatbot (Full-Stack Project with AI-Generated UI)11 lectures • 1hr 29min

Requirements

Description

Who this course is for: