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AI Integration for Java Developers

Trainer(s): Roy van Rijn, Makan Sepehrifar, duration: 2 days (2x 8 hours)

Introduction
This comprehensive training teaches Java developers how to enhance applications with AI capabilities using both AI-assisted development tools and traditional AI integration approaches. Participants will learn to leverage AI for accelerating development workflows through tools like GitHub Copilot, while also exploring how to implement AI features using cloud-based services and local models. This course bridges the gap between traditional Java development and the AI-enhanced development future.

Detailed description
After this 2-day training, you will confidently leverage AI in two complementary ways: as a development accelerator and as application functionality. You’ll master AI-assisted development tools like GitHub Copilot for writing better code faster, understanding effective prompting techniques and troubleshooting strategies. Additionally, you’ll learn to integrate AI capabilities into Java applications using modern libraries and frameworks, including working with Large Language Models (LLMs), implementing Retrieval Augmented Generation (RAG) systems, and building intelligent features that enhance user experiences.

The first day focuses on AI-assisted development workflows, covering GitHub Copilot setup, effective prompting for Java/Kotlin development, code review assistance, and testing strategies. You’ll also establish AI fundamentals for application integration and work with popular Java AI libraries. The second day advances to building AI-powered applications, covering advanced integration patterns including RAG systems, performance optimization, and production deployment considerations. You’ll build practical examples that demonstrate real-world AI integration scenarios commonly encountered in enterprise applications.

Target audience
This training is designed for Java developers, architects, and tech leads who want to leverage AI both for accelerating their development workflow and for implementing AI features in applications. No prior AI experience is required, but participants should have solid Java development experience and familiarity with REST APIs and modern Java frameworks. Some experience with GitHub and modern IDEs is beneficial for the AI-assisted development portions.

Learning goals

  • Understanding AI-assisted development capabilities and limitations
  • GitHub Copilot setup, configuration, and effective usage patterns
  • Prompt engineering fundamentals for development acceleration
  • Code review and refactoring strategies with AI assistance
  • Testing approaches in AI-assisted development workflows
  • AI fundamentals tailored for Java developers
  • Java AI libraries (DJL, Tribuo, Deeplearning4j) implementation
  • Cloud AI service integration (AWS Bedrock, Azure OpenAI, Google AI)
  • Local model deployment and management with Java
  • RAG (Retrieval Augmented Generation) application development
  • Embedding LLMs into existing enterprise applications
  • Performance optimization and cost management strategies
  • Testing and validation approaches for AI components
  • Production deployment patterns for AI-enhanced Java applications
  • Ethical considerations and best practices for AI development

Skills acquired in this training
Skills that AI Integration for Java Developers provides:

  • Proficiency with AI-assisted development tools for accelerated coding
  • Understanding of effective prompting techniques for development tasks
  • Experience with troubleshooting and optimizing AI development tools
  • Understanding of AI capabilities and limitations in Java applications
  • Proficiency with Java AI libraries and frameworks
  • Experience with both cloud and local AI model integration
  • Knowledge of RAG architecture and implementation
  • Understanding of AI performance optimization techniques
  • Practical experience with AI testing and validation
  • Awareness of cost optimization strategies for AI features
  • Knowledge of ethical considerations in AI development

The main focus is on acquiring the following skills:

  • Accelerating Java development workflows with AI assistance
  • Implementing AI features in Java applications effectively
  • Choosing appropriate AI integration approaches for specific use cases
  • Building maintainable and scalable AI-enhanced applications

Training outline
Day 1: AI-Assisted Development and Integration Fundamentals

  • The evolution of AI in software development and overview of AI-assisted tools (1 hour)
  • GitHub Copilot deep dive: setup, configuration, and effective prompting for Java/Kotlin (2 hours)
  • Practical exercises: code review, refactoring, and testing with AI assistance (1.5 hours)
  • AI fundamentals for Java developers and Java AI libraries overview (1.5 hours)
  • Hands-on: Building your first AI-enhanced Java application (2 hours)

Day 2: Advanced AI Integration and Production Patterns

  • Cloud AI services integration with Java (AWS Bedrock, Azure OpenAI, Google AI) (1.5 hours)
  • RAG (Retrieval Augmented Generation) systems with Java (2 hours)
  • Local model deployment strategies and performance optimization (1.5 hours)
  • Testing strategies for AI-enhanced applications and cost optimization (1.5 hours)
  • Production deployment patterns, ethical considerations, and best practices (1 hour)
  • Workshop: Enhancing an existing Java application with both AI assistance and AI capabilities (1.5 hours)

Provided training material
GitHub repositories with AI-assisted development examples, complete AI integration samples, cloud service integration templates, RAG implementation examples, GitHub Copilot configuration guides, effective prompting cheat sheets, and production deployment guides.

About the trainers
Roy van Rijn is a Java Champion and senior software engineer at OpenValue with over 15 years of experience in Java development. He’s passionate about exploring emerging technologies and has been actively working with AI integration in Java applications. Roy is a frequent speaker at international conferences and has contributed to various open-source projects.

Makan Sepehrifar is a software architect at Code Nomads with extensive experience in enterprise application development and AI integration. He works as a Senior Solution Architect at Rabobank in the Tech4Dev department, implementing GenAI for all development teams at the bank. Makan has hands-on experience with GitHub Copilot deployment and specializes in bridging the gap between traditional enterprise systems and modern AI capabilities, making him uniquely qualified to teach both AI-assisted development and AI application integration.

Practical details

Standard pricing for this training: EUR 1395,- ex VAT per attendee.
Please contact us for pricing for tailored content and for in house group trainings.

Trainings can be given in one of our offices (Utrecht, Amsterdam, Rotterdam, Arnhem, Munich, Dusseldorf, Vienna, Zurich), on site at a client location, or (in some cases) remote. Training content can be tailored to meet your specific requirements.

Want to enroll or have a question? Contact us via mail at info@openvalue.training, give us a call at +31-85-0606886 or use the form below.

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