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Duration 21 hours
Course Outline
Introduction to Vibe Coding
- Definition and evolution of vibe coding
- The concept of “prompt-to-code” collaboration
- Differences between AI-assisted coding and traditional development
Large Language Models in Coding
- Key LLMs for developers: GPT-4, DeepSeek, Qwen, Mistral
- Comparison of open-source versus proprietary AI coding tools
- Deployment of LLMs locally or via APIs
Prompt Engineering for Developers
- Effective prompting techniques for generating and refactoring code
- Managing context and conversation state
- Building reusable prompt templates for coding tasks
Hands-on Vibe Coding Environments
- Leveraging Replit for collaborative AI coding
- Integrating GitHub Copilot and Qwen Coder into IDEs
- Customising workflows for better team collaboration
Code Quality and Validation in AI Workflows
- Reviewing and testing code generated by LLMs
- Ensuring code consistency, maintainability, and security
- Incorporating code validation tools into the workflow
Enterprise Integration and Governance
- Scaling vibe coding practices across teams
- AI governance, ethics, and compliance in code generation
- Creating organisational frameworks for AI-assisted development
Advanced Topics: Extending Vibe Coding
- Combining multiple LLMs for hybrid AI workflows
- Integrating vibe coding with CI/CD automation
- Future trends: multi-agent development ecosystems
Team Project and Collaboration
- Designing a real-world AI-assisted coding project
- Collaborating with both human and AI developers
- Presenting outcomes and measuring productivity improvements
Summary and Next Steps
Requirements
- A solid grasp of software development workflows
- Practical experience with Python, JavaScript, or another contemporary programming language
- Proficiency in Git-based version control systems
Target Audience
- Software engineers exploring AI-assisted development methods
- Engineering leads overseeing AI adoption in coding processes
- Enterprise development teams aiming to integrate 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
