Developers can benefit from generative AI to increase productivity and develop innovative solutions faster. In this workshop, they learn how to specifically use AI tools such as ChatGPT and Claude to generate code, identify errors and create automated tests. This saves valuable time, optimizes the development process and increases the quality of the software. With practical applications, employees can directly benefit from the benefits of AI in everyday working life.

In this hands-on workshop, developers learn how to use generative AI tools such as ChatGPT, Claude, and others for their daily tasks. Attendees will gain insights into how to integrate and use these AI models to make their development more efficient, generate code, fix bugs, and create innovative solutions.

Benefits for you as a company:

• Increased productivity by using generative AI tools for code generation, troubleshooting, and automated documentation.
• Efficient problem solving through the use of AI to analyze code, identify weak points and suggest improvements.
• Training professionals by acquiring practical knowledge that familiarizes development teams with the latest AI technologies.
• Fostering innovation by discovering new ways of working and technologies that keep the company future-proof and competitive.
• Reduce costs by using AI to automate development processes and require less manual intervention.

1. Introduction to generative AI and its areas of application

• What is generative AI and how does it work?
• The importance of AI in software development and current market trends
• Overview of leading generative AI models: ChatGPT, Claude and Co.
• Areas of application: Automated code generation, text analysis, content creation and more
• How generative AI speeds up the development process and increases efficiency

2. Generative AI in software development: Potentials and possible uses

• Automating code generation: How generative AI helps developers write code faster and more error-free
• Preparation of documentation and comments using AI: Automating documentation processes and improving code quality
• Integrating AI into Existing Development Workflows: Tools and Frameworks to Help Developers Use Generative AI Seamlessly
• Case studies: Successful implementations of generative AI in software projects

3. ChatGPT, Claude & Co. in comparison: The right tools for different tasks

• Presentation of the most important generative AI models: differences and strengths of ChatGPT, Claude and other tools
• How to choose the right model for each development process: API integration, customization, and model optimization
• Advantages and disadvantages of ChatGPT compared to Claude and other generative AI models
• Best practices: When and how to use these tools sensibly in software development

4. Practical example 1: Automated code generation with ChatGPT

• How developers can use ChatGPT to create code snippets and features
• Step-by-step guide: How to generate working code for a specific task with ChatGPT
• Integration of generated code into existing projects and adaptation to individual requirements
• Problems and solutions in automated code generation: Failure analysis and debugging with generative AI

5. Practical example 2: Code refactoring and optimization with generative AI

• How AI helps analyze and improve existing code
• Demonstration of code refactoring techniques: Improving code quality through generative AI
• Using generative AI to identify optimization potential and improve performance
• Examples of using AI for code optimization in large software projects

6. Practical example 3: Test automation with generative AI

• Using AI to create automated tests for software applications
• Create unit tests and integration tests using ChatGPT or Claude
• How to use generative AI to automatically generate test cases and increase test coverage
• Analyze test data and improve test strategies with AI

7. Ethical Considerations and Challenges in Using Generative AI

• Risks and challenges when using generative AI in software development: bias, data quality, and reliability
• Ethics in AI: How to ensure that generative AI is used responsibly
• Data security and data protection: What developers need to consider when integrating generative AI into their projects
• Dealing with legal issues and copyrights related to AI-generated code

8. Integration of generative AI into the development process

• How to integrate generative AI tools into the software development cycle
• Automate development and deployment processes with the help of AI
• Developers' collaboration with AI: How to use AI as a co-developer while staying in control
• Optimizing the development process through AI-based automation

9th practical workshop: Generative AI in action

• Interactive exercises: participants create and optimize code using ChatGPT and Claude
• Joint work on real software development projects using generative AI
• Adapting AI-generated solutions to the individual requirements of participants
• Feedback session: Discussion of experiences and challenges during the workshop

10th outlook: The future of generative AI in software development

• Trends and developments in generative AI and their impact on software development
• How generative AI will continue to develop and what new features can be expected
• The role of generative AI in the future of software development: automation, personalization, and innovation
• How developers can further expand their capabilities in using generative AI to stay at the forefront of technological development

‍