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Duration 21 hours
Course Outline
Introduction to Vibe Coding
- Definition and historical context of vibe coding
- The philosophy behind “prompt-to-code” collaboration
- Distinguishing AI coding from traditional development methods
Large Language Models in Coding
- Developer overview of LLMs: GPT-4, DeepSeek, Qwen, Mistral
- Comparing open-source versus proprietary AI coders
- Deploying LLMs locally or via API endpoints
Prompt Engineering for Developers
- Effective prompting techniques for code generation and refactoring
- Managing context and handling conversation state
- Developing 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 for team-based collaboration
Code Quality and Validation in AI Workflows
- Reviewing and testing code generated by LLMs
- Ensuring consistency, maintainability, and security standards
- Embedding code validation tools within the workflow
Enterprise Integration and Governance
- Scaling vibe coding practices across teams
- Addressing AI governance, ethics, and compliance in code generation
- Designing 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 alongside human and AI developers
- Presenting outcomes and measuring productivity improvements
Summary and Next Steps
Requirements
- A solid understanding of software development workflows
- Practical experience with Python, JavaScript, or another contemporary programming language
- Proficiency with Git-based version control systems
Target Audience
- Software engineers exploring AI-assisted development practices
- Engineering leads overseeing the adoption of AI in coding workflows
- Enterprise development teams looking 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
