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Duration 21 hours
Course Outline
Foundations of Vibe Coding
- Defining the concept and historical context of vibe coding
- The philosophy behind “prompt-to-code” collaboration
- Distinguishing AI coding from traditional development methods
Large Language Models in Development
- An overview of developer-focused LLMs: GPT-4, DeepSeek, Qwen, Mistral
- Evaluating open-source versus proprietary AI coding solutions
- Local deployment or API-based integration of LLMs
Prompt Engineering for Developers
- Crafting effective prompts for code generation and refactoring
- Managing context and maintaining conversation state
- Developing reusable prompt templates for common coding tasks
Practical Vibe Coding Environments
- Leveraging Replit for collaborative AI coding sessions
- Embedding GitHub Copilot and Qwen Coder into IDEs
- Tailoring workflows to support team collaboration
Maintaining Code Quality in AI Workflows
- Reviewing and testing code generated by LLMs
- Prioritizing consistency, maintainability, and security
- Incorporating code validation tools into the development cycle
Enterprise Adoption and Governance
- Scaling vibe coding practices across distributed teams
- Navigating AI governance, ethics, and compliance in code generation
- Establishing organizational frameworks for AI-assisted development
Advanced Concepts: Expanding Vibe Coding
- Orchestrating multiple LLMs for hybrid AI workflows
- Automating vibe coding processes within CI/CD pipelines
- Future outlook: multi-agent development ecosystems
Team Collaboration and Project Work
- Designing a real-world project utilizing AI-assisted coding
- Fostering collaboration between human developers and AI tools
- Presenting outcomes and quantifying productivity improvements
Recap and Path Forward
Requirements
- A solid grasp of standard software development workflows
- Practical experience with Python, JavaScript, or other contemporary programming languages
- Proficiency in Git-based version control systems
Target Audience
- Software engineers seeking to explore AI-assisted development practices
- Engineering leaders responsible for managing AI adoption within coding processes
- Enterprise teams looking to integrate 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
