Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Duration 21 hours
Course Outline
Introduction to Vibe Coding
- Definition and evolution of vibe coding
- The philosophy behind “prompt-to-code” collaboration
- Distinguishing AI-assisted coding from traditional development methods
Large Language Models in Coding
- Overview of developer-focused LLMs: GPT-4, DeepSeek, Qwen, Mistral
- Comparing open-source and proprietary AI coding tools
- Deploying LLMs locally or through APIs
Prompt Engineering for Developers
- Effective prompting techniques for code generation and refactoring
- Managing context and handling conversation states
- Building reusable prompt templates for various coding tasks
Hands-on Vibe Coding Environments
- Leveraging Replit for collaborative AI coding
- Integrating GitHub Copilot and Qwen Coder into IDEs
- Customizing workflows to enhance team 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
- Ensuring AI governance, ethics, and compliance in code generation
- Building organizational frameworks for AI-assisted development
Advanced Topics: Extending Vibe Coding
- Combining multiple LLMs for hybrid AI workflows
- Integrating vibe coding with CI/CD automation
- Future trends: multi-agent development ecosystems
Team Project and Collaboration
- Designing a practical, real-world AI-assisted coding project
- Collaborating effectively with both human and AI developers
- Presenting outcomes and assessing productivity improvements
Summary and Next Steps
Requirements
- A solid grasp of software development workflows
- Proficiency in Python, JavaScript, or other modern programming languages
- Knowledge of Git-based version control systems
Target Audience
- Software engineers exploring AI-assisted development practices
- Engineering leads managing AI adoption in coding processes
- Enterprise teams aiming to integrate 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
