AI Integration for Java Developers

Introduction
AI is no longer just an autocomplete tool. Modern AI systems can plan work, decompose tasks and generate working code, marking the shift toward agentic development and fundamentally changing what software development looks like and what the role of a developer becomes.

Detailed description

AI is rapidly changing the way software is built. Modern AI systems can plan work, break down complex tasks, call external tools and generate working code within a single workflow. This fundamentally shifts what software development looks like and what makes a developer effective.

The first part of the training explores how we arrived at this point. We trace the evolution of AI from early rule based approaches, through the deep learning breakthroughs of the 2010s, into the augmentation era of tools such as Copilot and chat assistants, and finally into the current agentic era.

Rather than focusing only on tools, we examine how the nature of software development itself is changing. The primary bottleneck is no longer writing code. Instead it has shifted toward clearly specifying problems, reviewing generated output and integrating solutions into larger systems.

This shift changes the role of the developer. Instead of focusing mainly on writing code line by line, developers increasingly act as directors of the development process. They define problems, guide AI systems and ensure that generated solutions meet quality and architectural requirements.

An important part of this transition is understanding the limits of AI. High velocity code generation without careful review can quickly introduce risks. During the training we discuss how to recognize what AI systems structurally miss, how to create strong feedback loops and how to introduce guardrails that maintain quality beyond traditional code review.

The second half of the day focuses on practical application. Participants work hands on with agentic workflows and learn how to break down features into smaller tasks that AI systems can execute effectively. We explore how to structure context for models, how to work model agnostically using tools such as OpenCode and OpenSpec and which principles make agentic development reliable rather than chaotic.

The training concludes with a look ahead at emerging developments such as agent teams and how autonomous AI collaboration may further influence the future of software development.

Prerequisites
You’ll need solid Java development experience and familiarity with REST APIs and modern frameworks. Some experience with GitHub and modern IDEs helps for the AI-assisted development portions. No prior AI experience required. We’ll build that foundation together.

Target audience
Designed for Java developers, architects, and tech leads who recognize that AI is transforming both how we build software and what we can build. Whether you’re exploring AI capabilities, planning AI features for your applications, or leading teams that need to adopt AI tools, this training gives you practical expertise that’s immediately applicable.

Learning goals

You’ll gain comprehensive AI expertise:

• Understand how AI has evolved from rule based systems to modern agentic workflows
• Understand how AI is changing the role of developers from writing code to specifying, reviewing and integrating solutions
• 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

Topics covered

Master the complete AI toolkit:

• Understanding the evolution of AI assisted software development and the rise of agentic workflows
• 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

• Introduction to the evolution of AI in software development
• From rule based systems to the deep learning era and modern AI development tools
• The transition from AI assisted coding to agentic development workflows
• Understanding how AI changes the structure of software development work
• The shifting role of developers from writing code to specifying, reviewing and integrating solutions
• Capabilities and limitations of AI generated code
• GitHub Copilot setup, configuration and effective usage patterns
• Prompt engineering techniques for development tasks
• Using AI for code generation, refactoring and development acceleration
• Code review strategies for AI generated code
• Testing approaches in AI assisted development workflows
• Establishing guardrails and feedback loops for reliable AI assisted development
• Hands on exercises with modern AI development workflows
• Breaking down development tasks and experimenting with AI assisted approaches
• Preview of emerging developments such as agent teams

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)

Course format
This is an in-person classroom training that can be delivered at an OpenValue office or as an in-company training.

Certification
Participants receive a certificate of completion upon finishing the training.

Next steps
For more information about expanding your knowledge past this course, check out our entire training portfolio at www.openvalue.training or in your learning management system. Contact us at training@openvalue.nl for personal learning advice or customized on-demand training and just contact your OpenValue trainer during the training course.

Provided training material
Learning material with slides and exercises will be available for the participants.

About the trainers
Tom Wigleven is Principal Engineer at OpenValue. Mauro Palsgraaf is Senior Software Developer at OpenValue.

Note: This training can be given in Dutch or English at one of the OpenValue offices (Utrecht, Amsterdam, Rotterdam, Arnhem, Munich, Dusseldorf, Vienna, Zurich) or at your own location. Please contact us to discuss possibilities for a remote training and for training in German.