Vibecoding – AI-powered Software Development in Practice

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.