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Course Outline
Introduction to Vibe Coding
- Definition and origins of vibe coding
- The philosophy of “prompt-to-code” collaboration
- Distinguishing AI coding from conventional development
Large Language Models in Coding
- Developer-focused overview of LLMs: GPT-4, DeepSeek, Qwen, Mistral
- Comparing open-source versus proprietary AI coders
- Local deployment or API-based usage of LLMs
Prompt Engineering for Developers
- Effective prompting techniques for code generation and refactoring
- Managing context and handling conversation state
- Building 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
- 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
- Incorporating code validation tools into the workflow
Enterprise Integration and Governance
- Scaling vibe coding across teams
- 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 with both human and AI developers
- Presenting results and measuring productivity gains
Summary and Next Steps
Requirements
- A solid grasp of software development processes
- Proficiency in Python, JavaScript, or another contemporary programming language
- Knowledge of Git-based version control systems
Target Audience
- Software engineers looking into AI-assisted development
- Engineering leaders managing AI adoption in coding workflows
- Enterprise development teams aiming to integrate LLMs into production pipelines
21 Hours
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