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Duration 21 hours
Course Outline
Introduction to Vibe Coding
- Defining the concept and exploring its origins
- The philosophy of “prompt-to-code” collaboration
- Distinguishing AI-driven coding from traditional development
Large Language Models in Coding
- Key LLMs for developers: GPT-4, DeepSeek, Qwen, Mistral
- Evaluating open-source versus proprietary AI coding tools
- Deploying LLMs locally or through API services
Prompt Engineering for Developers
- Techniques for generating and refactoring code through effective prompting
- Managing context and handling conversation states
- 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
- Tailoring workflows to enhance team collaboration
Code Quality and Validation in AI Workflows
- Reviewing and testing code generated by LLMs
- Safeguarding consistency, maintainability, and security
- Incorporating code validation tools into the workflow
Enterprise Integration and Governance
- Scaling vibe coding practices across teams
- Navigating AI governance, ethics, and compliance in code generation
- Creating organizational frameworks for AI-assisted development
Advanced Topics: Extending Vibe Coding
- Combining multiple LLMs for hybrid AI workflows
- Merging 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 effectively with human and AI developers
- Presenting outcomes and assessing productivity improvements
Summary and Next Steps
Requirements
- A solid understanding of software development workflows
- Proficiency in Python, JavaScript, or another modern programming language
- Working knowledge of Git-based version control systems
Audience
- Software engineers looking to explore AI-assisted development
- Engineering leads overseeing AI adoption in coding workflows
- 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
