Generative AI for Java and Spring Developers Specialization Course Syllabus

Overview: This specialization is designed for Java and Spring developers looking to integrate Generative AI into enterprise-grade backend systems. Over approximately 30 hours of content across 4 courses, you'll progress from foundational concepts to advanced pipeline integration, combining theory with hands-on labs. Each module includes real-world projects focused on security, testing, CI/CD, and production deployment of AI-powered Java services.

Module 1: Generative AI for Java and Spring Development

Estimated time: 11 hours

• Integrate Spring Boot with Generative AI APIs
• Set up dependencies for LLM integration in Java applications
• Configure OpenAI client in a Spring environment
• Build a Java/Spring app for text and image generation using ChatGPT
• Implement entity extraction and context management in AI workflows

Module 2: Introduction to Generative AI and Applications

Estimated time: 7 hours

• Understand foundation models vs. discriminative models
• Explore GenAI use cases in text, code, and visual generation
• Compare outputs of different large language models
• Prototype basic GenAI features in a Java environment

Module 3: Prompt Engineering Basics for Java Developers

Estimated time: 9 hours

• Apply zero-shot and few-shot prompting techniques
• Use chain-of-thought reasoning patterns in prompts
• Design prompt templates for Java-based workflows
• Refine prompts for logging enhancement, code generation, and data summarization

Module 4: Architecting AI-Powered Java Pipelines

Estimated time: 11 hours

• Integrate Generative AI into CI/CD pipelines
• Automate unit test generation using AI
• Perform AI-driven code reviews and security scanning
• Generate architectural diagrams using LLMs

Module 5: DevSecOps and Production AI Integration

Estimated time: 8 hours

• Apply DevSecOps practices to AI-enhanced services
• Secure AI-powered microservices in production
• Deploy and monitor AI-integrated Spring applications

Module 6: Final Project

Estimated time: 10 hours

• Develop a full-stack Java/Spring microservice with AI capabilities
• Implement secure LLM integration with prompt engineering
• Deploy the service with CI/CD automation and AI-generated test coverage

Prerequisites

• Strong working knowledge of Java programming
• Familiarity with Spring Framework and Spring Boot
• Experience building and deploying backend services

What You'll Be Able to Do After

• Implement AI-driven features in Java applications using Spring
• Integrate large language models via OpenAI API for real-world tasks
• Apply prompt engineering best practices in enterprise Java workflows
• Architect secure, scalable AI pipelines with CI/CD and DevSecOps
• Deploy production-ready AI-enhanced Spring microservices