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Duration 21 hours (3 days)
Course Outline
Introduction to Vibe Coding
- Origins and definition of vibe coding
- The "prompt-to-code" collaboration philosophy
- Distinguishing AI coding from traditional development methods
Large Language Models in Coding
- Developer-focused LLMs: GPT-4, DeepSeek, Qwen, Mistral
- Analysis of open-source versus proprietary AI coding tools
- Strategies for deploying LLMs locally or via APIs
Prompt Engineering for Developers
- Techniques for effective code generation and refactoring prompts
- Managing context and conversation states
- Building reusable prompt templates for common coding tasks
Hands-on Vibe Coding Environments
- Leveraging Replit for collaborative AI coding
- Integrating GitHub Copilot and Qwen Coder into IDEs
- Tailoring workflows for enhanced team collaboration
Code Quality and Validation in AI Workflows
- Testing and reviewing code generated by LLMs
- Maintaining consistency, maintainability, and security
- Incorporating code validation tools into the development process
Enterprise Integration and Governance
- Scaling vibe coding practices across organizational teams
- Addressing AI governance, ethics, and compliance in code generation
- Developing frameworks for AI-assisted development at the organizational level
Advanced Topics: Extending Vibe Coding
- Utilizing multiple LLMs for hybrid AI workflows
- Connecting vibe coding with CI/CD automation
- Emerging trends: multi-agent development ecosystems
Team Project and Collaboration
- Designing a practical, real-world AI-assisted coding project
- Coordinating work between human and AI developers
- Presenting outcomes and quantifying productivity improvements
Summary and Next Steps
Requirements
- Basic knowledge of software development workflows
- Practical experience with Python, JavaScript, or other modern programming languages
- Working familiarity with Git-based version control systems
Target Audience
- Software engineers interested in AI-assisted development
- Engineering leaders managing the adoption of AI in coding processes
- Enterprise development teams looking to incorporate LLMs into production pipelines
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
