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Duration 21 hours
Course Outline
Introduction to Vibe Coding
- Origins and definition of vibe coding
- The concept of “prompt-to-code” collaboration
- Distinguishing AI coding from conventional development
Large Language Models in Coding
- Developer-focused LLM overview: GPT-4, DeepSeek, Qwen, Mistral
- Evaluating open-source vs. proprietary AI coding tools
- Local deployment or API access for LLMs
Prompt Engineering for Developers
- Crafting effective prompts for code generation and refactoring
- Managing context and maintaining conversation state
- Building reusable prompt templates for common coding tasks
Hands-on Vibe Coding Environments
- Leveraging Replit for collaborative AI coding
- Incorporating GitHub Copilot and Qwen Coder into IDEs
- Tailoring workflows for enhanced team collaboration
Code Quality and Validation in AI Workflows
- Testing and reviewing code generated by LLMs
- Maintaining consistency, maintainability, and security
- Embedding code validation tools into the workflow
Enterprise Integration and Governance
- Scaling vibe coding practices across teams
- Governance, ethics, and compliance in AI code generation
- Establishing organizational frameworks for AI-assisted development
Advanced Topics: Extending Vibe Coding
- Combining multiple LLMs for hybrid AI workflows
- Connecting vibe coding with CI/CD automation
- Emerging trends: multi-agent development ecosystems
Team Project and Collaboration
- Structuring a real-world AI-assisted coding project
- Collaborating with both human and AI developers
- Demonstrating results and assessing productivity improvements
Summary and Next Steps
Requirements
- A solid understanding of standard software development processes
- Proficiency in Python, JavaScript, or another contemporary programming language
- Experience with Git-based version control systems
Target Audience
- Software engineers interested in AI-assisted development
- Engineering leaders managing AI integration into coding practices
- Enterprise teams aiming to incorporate LLMs into their 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
