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Course Outline
Introduction to Vibe Coding
- Defining vibe coding and tracing its origins
- The philosophy behind "prompt-to-code" collaboration
- Distinguishing AI-driven development from traditional methods
Understanding Large Language Models in Coding
- Key LLMs for developers: GPT-4, DeepSeek, Qwen, Mistral
- Evaluating open-source versus proprietary AI coding tools
- Deploying LLMs locally or through APIs
Prompt Engineering for Developers
- Crafting effective prompts to generate and refactor code
- Managing context and handling conversation states
- Building reusable prompt templates for various coding tasks
Practical Vibe Coding Environments
- Utilizing Replit for collaborative AI coding
- Integrating GitHub Copilot and Qwen Coder into IDEs
- Tailoring workflows to enhance team collaboration
Ensuring Code Quality and Validation in AI Workflows
- Reviewing and testing code generated by LLMs
- Maintaining consistency, maintainability, and security
- Incorporating code validation tools into the workflow
Enterprise Integration and Governance
- Scaling vibe coding practices across teams
- Navigating AI governance, ethics, and compliance in code generation
- Establishing organizational frameworks for AI-assisted development
Advanced Topics: Expanding Vibe Coding Capabilities
- Leveraging multiple LLMs for hybrid AI workflows
- Merging vibe coding with CI/CD automation
- Exploring future trends: multi-agent development ecosystems
Team Project and Collaboration
- Designing a real-world AI-assisted coding project
- Collaborating effectively with both human and AI developers
- Presenting outcomes and evaluating productivity improvements
Summary and Next Steps
Requirements
- Solid understanding of software development workflows
- Practical experience with Python, JavaScript, or other contemporary programming languages
- Familiarity with Git-based version control systems
Target Audience
- Software engineers keen on exploring AI-assisted development
- Engineering leads managing the adoption of AI in coding processes
- Enterprise development teams looking to incorporate 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
