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Duration 21 hours (3 days)
Course Outline
Foundations of Vibe Coding
- Origins and definition of vibe coding
- The concept of “prompt-to-code” collaboration
- Distinguishing AI coding from conventional development methods
Utilizing Large Language Models for Coding
- Key LLMs for developers: GPT-4, DeepSeek, Qwen, and Mistral
- Evaluating open-source versus proprietary AI coding tools
- Local deployment of LLMs or access via APIs
Developer-Focused Prompt Engineering
- Optimizing prompts for code generation and refactoring
- Managing context and handling conversation states
- Building reusable prompt templates for common coding tasks
Practical Vibe Coding Environments
- Leveraging Replit for collaborative AI coding
- Embedding GitHub Copilot and Qwen Coder within IDEs
- Tailoring workflows to enhance team collaboration
Quality Assurance and Validation in AI Workflows
- Reviewing and testing code generated by LLMs
- Safeguarding consistency, maintainability, and security
- Embedding code validation tools into the workflow
Enterprise Adoption and Governance
- Scaling vibe coding practices across teams
- Addressing AI governance, ethics, and compliance in code generation
- Establishing organizational frameworks for AI-assisted development
Advanced Strategies: Expanding Vibe Coding
- Orchestrating multiple LLMs for hybrid AI workflows
- Merging vibe coding with CI/CD automation
- Emerging trends: multi-agent development ecosystems
Collaborative Team Project
- Designing a practical AI-assisted coding project
- Coordinating efforts between human and AI developers
- Presenting outcomes and quantifying productivity improvements
Conclusion and Future Steps
Requirements
- Foundational knowledge of software development workflows
- Practical experience with Python, JavaScript, or another contemporary programming language
- Proficiency with Git-based version control systems
Target Audience
- Software engineers investigating AI-assisted development practices
- Engineering leads managing AI integration within coding processes
- Enterprise development teams aiming to embed LLMs into 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
