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Course Outline
Introduction to Vibe Coding
- Definition and history of vibe coding.
- The philosophy behind “prompt-to-code” collaboration.
- How AI coding differs from traditional development methods.
Large Language Models in Coding
- Overview of LLMs for developers, including GPT-4, DeepSeek, Qwen, and Mistral.
- Comparing open-source versus proprietary AI coders.
- Deploying LLMs locally or via APIs.
Prompt Engineering for Developers
- Effective prompting techniques for generating and refactoring code.
- Managing context and handling conversation state.
- Creating reusable prompt templates for various coding tasks.
Hands-on Vibe Coding Environments
- Using Replit for collaborative AI coding.
- Integrating GitHub Copilot and Qwen Coder into IDEs.
- Customizing workflows to enhance team collaboration.
Code Quality and Validation in AI Workflows
- Reviewing and testing code generated by LLMs.
- Ensuring consistency, maintainability, and security.
- Integrating code validation tools into the workflow.
Enterprise Integration and Governance
- Scaling vibe coding 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 with both human and AI developers.
- Presenting results and measuring productivity gains.
Summary and Next Steps
Requirements
- A solid understanding of software development workflows.
- Experience with Python, JavaScript, or another modern programming language.
- Familiarity with Git-based version control systems.
Audience
- Software engineers exploring AI-assisted development.
- Engineering leads overseeing the adoption of AI in coding workflows.
- Enterprise development teams looking 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
