🚀 Unlock the Future of AI Development with Java and Spring Boot
Are you a Java developer eager to dive into the world of Generative AI? This comprehensive course teaches you how to build intelligent applications using Spring Boot, Spring AI, and leading AI providers like OpenAI, Hugging Face, Cohere, and more.
Whether you’re building a chatbot, AI content generator, or intelligent document analyzer, this course gives you the full-stack knowledge to connect LLMs (Large Language Models) with Java applications, securely and efficiently.
From setup to deployment, you’ll master how to bring Generative AI to life with Java.
✅ What You’ll Learn:
-
Introduction to Generative AI & LLMs for Java Developers
-
Setting up Spring Boot with Spring AI
-
Connecting to OpenAI, Hugging Face, and other providers
-
Building AI-powered REST APIs and microservices
-
Integrating AI into real-world use cases (chatbots, summarizers, Q&A apps)
-
Token management, rate-limiting & security best practices
-
Deploying Java + AI applications to the cloud (AWS/GCP)
✅ Key Technologies Covered:
-
Java 17+
-
Spring Boot 3+
-
Spring AI (formerly Spring for GenAI)
-
OpenAI, Hugging Face, Azure OpenAI
-
LangChain4j (Optional)
-
RESTful APIs
-
Maven & Gradle
-
Docker & Cloud deployment options
1 - Introduction to Spring AI
2 - Working with AI Models in Spring
-
41 - Git Repo details
-
51 -What is LLM
-
62 -Spring AI Documentation
-
73 -OpenAI ChatGPT Walkthrough
-
84 -Generating Spring AI Project.mp4
-
95 -OpenAI API Key and Walkthrough
-
106 -Setting Application with API Key
-
117 -Calling ChatClient with Controller
-
128 -API Call with ChatResponse Object
-
139 -API with PromptTemplate
-
1410 -API with String Template of Prompts
-
1511 -Roles in Prompts for API
-
1612 -API with Prompt and data
-
1713 -OutputConverters and BeanOutputConverter example with API
-
1814 -ListOutputConverter and Entity handling in API
3 - Spring AI with DALL-E and Audio Models (MultiModality)
4 - Retrieval-Augmented Generation (RAG) with Spring AI
-
241 -What is RAG
-
252 -Introduction to Vector and Vector Database
-
263 RAG Implementation Overview
-
274 -SimpleVector Store Imlementation for RAG
-
285 -Understanding Tokens for LLM Models
-
296 Loading Simple Vector Store in App
-
307 Implementing API to use Simple Vector Store for RAG
-
318 -Adding PGVector Store in Application
-
329 -Loading Data to PGVector Store for RAG
-
3310 -Implementing SimilaritySearch with PGVector Store for RAG
5 - Building AI driven Application
-
341 -Application Overview
-
352 -Creating Frontend App and setting up config
-
363 -Adding Feedback Form in UI
-
374 -Adding Feedback History in UI
-
385 -Creating AI driven Spring Boot Application
-
396 -Fetching Feedbcak API
-
407 -Save Feedback API
-
418 -Implement Save Feedback in UI
-
429 -Implement Feedback History in UI
-
4310 -Enhancing UI of Application
Lessons:
