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Vibe Coding – AI-Powered Software Development in Practice
From idea to prototype: Develop efficiently with Copilot, Cursor, Lovable, and MCP
Online
2 days
German
€ 1.390,-
plus VAT.
€ 1.654,10
incl. VAT.
Booking number
42721
Venue
Online
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5 dates
€ 1.390,-
plus VAT.
€ 1.654,10
incl. VAT.
Booking number
42721
Venue
Online
5 dates
plus VAT.
incl. VAT.
Booking number
Venue
0 Dates
Become a certified
Machine Learning Engineer
Machine Learning Engineer
This course is part of the certified Master Class "Machine Learning Engineer". If you book the entire Master Class, you save over 15 percent compared to booking this individual module.
To the Master Class
In-house training
In-house training just for your employees - exclusive and effective.
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In cooperation with
In cooperation with
In this hands-on training about Vibecoding as a modern approach to AI-powered software development. You’ll discover how to quickly turn ideas into working prototypes and robust pilots—in a structured, reproducible, and team-friendly way.
The focus is on efficient workflows that allow you to deploy AI assistants specifically for requirements, design, implementation, and debugging. You’ll work with modern tools like GitHub Copilot, Cursor, and Lovable and learn how to integrate them effectively into existing development processes—from Git and code reviews to testing and deployment.
Another key focus is on using MCP (Model Context Protocol) to connect AI assistants to internal systems, documentation, and interfaces in a controlled manner. This allows you to develop not only faster, but also more securely and sustainably.
Contents
1. Fundamentals and Principles of Vibecoding
- Mindset and approach: rapid feedback loops, small increments.
- Applications: Prototypes, pilot projects, and internal tools.
- Team roles: Department, Product, Development, QA, and Security.
- Define requirements and success criteria.
2. Toolset and Setup
- IDE-based assistance vs. chat-based use.
- An overview of GitHub Copilot, Cursor, and Lovable.
- Context Control: Rules, Style Guides, and Project Context.
- Working with repositories and making your first AI-assisted changes.
3. Prototyping with AI
- Rapid prototyping with Lovable.
- Prompting patterns: Spec-first, Plan-then-code, Iterate-with-diffs.
- Development of the first functional applications.
4. Prompt Engineering for Development
- Structured prompts for code, tests, and documentation.
- Definition of requirements, constraints, and quality criteria.
- Debugging with AI: A Systematic Approach and Error Analysis.
- Expansion of existing codebases, including tests.
5. Teamwork
- Working Agreements and Role Assignment.
- Code reviews with AI support.
- Ensure the quality and readability of AI-generated code.
6. Quality Assurance and Testing
- Testing strategies: unit, integration, and end-to-end testing.
- Using AI to generate test cases.
- Refactoring and stabilizing applications.
7. From Concept to Production
- Transition from prototype to production-ready solution.
- Deployment and release of applications.
- Best practices for sustainable development.
8. MCP and Context Integrations
- Use of MCP to integrate internal systems and data.
- Access to documentation, APIs, and tools.
- Controlled workflows to prevent misinterpretations.
- Implementing integrations in practice.
9. Capstone Project
- Developing our own pilot program in a short period of time.
- Review, quality assurance, and documentation.
- Identification of specific next steps toward production operation.
Learning environment
In your online learning environment, you will find useful information, downloads and extra services for this training course once you have registered.
Your benefit
- You'll learn how to integrate AI-driven development into your daily workflow in a structured and productive way.
- You quickly develop working prototypes and pilot projects based on your own ideas.
- You use tools such as Copilot, Cursor, and Lovable specifically for development, testing, and debugging.
- You understand how to integrate AI into existing development processes such as Git, code reviews, and CI/CD.
- You will learn how to establish secure and controlled connections to internal systems via MCP.
- You'll learn practical best practices for quality, security, and sustainable AI-driven development.
trainer
No items found.
Methods
The training concise theoretical insights with a strong emphasis on practical application:
- Live demos and structured introductions to tools and workflows
- Guided hands-on exercises and code-along sessions
- Working on realistic sample projects
- Step-by-step development of a custom pilot as part of a final project
The free versions of the tools training sufficient for this training .
Final examination
Recommended for
This training is designed for anyone who wants to put AI-powered development into practice:
- developers who want to use AI assistance in a structured and production-ready manner
- Product owners, business analysts process managers who want to develop prototypes and pilot projects more quickly
- Teams that want to integrate modern development approaches using AI into their processes
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