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Course Outline
Introduction to Vibe Coding
- Defining vibe coding and tracing its origins
- The concept of "prompt-to-code" collaborative development
- Distinguishing AI coding from conventional development practices
Large Language Models in Coding
- An overview of LLMs for developers: GPT-4, DeepSeek, Qwen, Mistral
- Evaluating open-source versus proprietary AI coding tools
- Local deployment of LLMs or utilisation via APIs
Prompt Engineering for Developers
- Techniques for effective prompting to generate and refactor code
- Managing context and handling conversation state
- Developing reusable prompt templates for various coding tasks
Hands-on Vibe Coding Environments
- Leveraging Replit for collaborative AI-driven coding
- Embedding GitHub Copilot and Qwen Coder into Integrated Development Environments
- Tailoring workflows to enhance team collaboration
Code Quality and Validation in AI Workflows
- Assessing and testing code generated by LLMs
- Maintaining consistency, maintainability, and security standards
- Incorporating code validation tools into the development workflow
Enterprise Integration and Governance
- Scaling vibe coding practices across multiple teams
- Addressing AI governance, ethics, and compliance in code generation
- Building organisational frameworks for AI-assisted development
Advanced Topics: Extending Vibe Coding
- Utilising multiple LLMs to create hybrid AI workflows
- Connecting vibe coding with CI/CD automation
- Emerging trends: multi-agent development ecosystems
Team Project and Collaboration
- Structuring a practical, AI-assisted coding project
- Coordinating work between human and AI developers
- Presenting outcomes and evaluating productivity improvements
Summary and Next Steps
Requirements
- A solid grasp of standard software development workflows
- Practical experience with Python, JavaScript, or other contemporary programming languages
- Working knowledge of Git-based version control systems
Target Audience
- Software engineers investigating AI-assisted development methods
- Engineering leads responsible for supervising the adoption of AI in coding processes
- Enterprise development teams looking to embed LLMs into their 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