The main goal of this course is to give developers a clear and professional insight into artificial intelligence in software development. Participants learn how modern AI systems, such as Large Language Models (LLMs), work and how to use them in a targeted manner in their daily workflow.

The focus is on using AI tools such as GitHub Copilot, Cursor, JetBrains AI and chat-based models correctly and responsibly. In addition, special attention is paid to the risks of security, privacy and dealing with business-sensitive code.

At the end of this course, participants can use AI as a productivity enhancer without sacrificing code quality, safety, or professional insight.

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This course is intended for professional software developers who want to integrate AI into their daily work in a safe and thoughtful way.

Participants have:

• Practical programming experience in at least one language (such as Java, C#, Python, JavaScript,...)
• Basic knowledge of version control (Git)
• Experience with an IDE (IntelliJ, VS Code, Eclipse,...)

The course focuses on developers who want to use AI as a tool without relinquishing their technical responsibility.

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The course is offered through instructor-led lessons with a strong focus on practice.

Participants actively work with various AI tools and carry out targeted exercises around:

• Code generation and refactoring
• Test generation and debugging
• AI-supported code reviews
• Critical review of AI output

Security and privacy are integrated into the exercises. Participants learn what information they can and cannot share with AI tools and how to limit the risks of data leaks and confidential code.

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AI Foundations
How Do Large Language Models Work? Terms such as tokens, context window, hallucinations and model differences explained from a developer perspective.

AI strengths and limitations
When does AI accelerate your workflow, and when does it introduce risks or errors?

AI developer tools
Overview and comparison of tools such as GitHub Copilot, Cursor, JetBrains AI, and chat-based models.

Prompting for developers
Effective prompts for code generation, refactoring, debugging, and test generation.

AI in the development workflow
AI as a pair programmer, learning aid and code review support.

Security, privacy and professional use
Risks related to business-sensitive information, data leaks, compliance and responsible use of AI in professional environments.

Evaluating AI output
Techniques to critically analyse, test and validate AI-generated code.

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