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Duration 21 hours
Course Outline
Introduction to Vibe Coding
- Definition and evolution of vibe coding.
- The concept of "prompt-to-code" collaboration.
- Distinguishing AI coding from traditional development.
Large Language Models in Coding
- Developer perspective on LLMs: GPT-4, DeepSeek, Qwen, Mistral.
- Comparing open-source vs. proprietary AI coding tools.
- Local deployment of LLMs vs. API-based access.
Prompt Engineering for Developers
- Crafting effective prompts for code generation and refactoring.
- Managing context and conversation state.
- Building reusable prompt templates for coding tasks.
Hands-on Vibe Coding Environments
- Leveraging Replit for collaborative AI coding.
- Embedding GitHub Copilot and Qwen Coder into IDEs.
- Customizing workflows for team-based collaboration.
Code Quality and Validation in AI Workflows
- Reviewing and testing code generated by LLMs.
- Maintaining consistency, maintainability, and security standards.
- Incorporating code validation tools into the workflow.
Enterprise Integration and Governance
- Scaling vibe coding practices across teams.
- Addressing AI governance, ethics, and compliance in code generation.
- Creating organizational frameworks for AI-assisted development.
Advanced Topics: Extending Vibe Coding
- Combining multiple LLMs for hybrid AI workflows.
- Linking vibe coding with CI/CD automation.
- Future trends: Multi-agent development ecosystems.
Team Project and Collaboration
- Designing a real-world AI-assisted coding project.
- Collaborating effectively with human and AI counterparts.
- Presenting outcomes and assessing productivity improvements.
Conclusion and Next Steps
Requirements
- Familiarity with standard software development processes.
- Proficiency in Python, JavaScript, or another contemporary programming language.
- Working knowledge of Git-based version control systems.
Target Audience
- Software engineers exploring AI-assisted development methods.
- Engineering leaders responsible for adopting AI in coding processes.
- Enterprise teams aiming 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
