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Duration 21 hours
Course Outline
Introduction to Vibe Coding
- Defining vibe coding and its historical context
- The philosophy of collaborative “prompt-to-code” development
- Distinguishing AI coding from traditional development methods
Large Language Models in Coding
- Overview of developer-focused LLMs: GPT-4, DeepSeek, Qwen, Mistral
- Comparison of open-source versus proprietary AI coding tools
- Methods for deploying LLMs locally or through APIs
Prompt Engineering for Developers
- Techniques for effective prompting to generate and refactor code
- Managing context and handling conversation state
- Developing reusable prompt templates for coding tasks
Hands-on Vibe Coding Environments
- Utilizing Replit for collaborative AI coding
- Integrating GitHub Copilot and Qwen Coder into IDEs
- Customizing workflows to support team collaboration
Code Quality and Validation in AI Workflows
- Reviewing and testing code generated by LLMs
- Maintaining consistency, maintainability, and security
- Incorporating code validation tools into the workflow
Enterprise Integration and Governance
- Scaling vibe coding practices across teams
- Addressing 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
- 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 assessing productivity improvements
Summary and Next Steps
Requirements
- A solid understanding of software development processes
- Proficiency in Python, JavaScript, or another contemporary programming language
- Knowledge of Git-based version control systems
Target Audience
- Software engineers interested in AI-assisted development
- Engineering leaders managing AI adoption in coding practices
- Enterprise development teams aiming to embed 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
