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 historical context of vibe coding
- The philosophy of “prompt-to-code” collaboration
- Distinguishing AI coding from traditional development practices
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 via API services
Prompt Engineering for Developers
- Techniques for effective prompting to generate and refactor code
- Managing context and handling conversation states
- Developing reusable prompt templates for coding tasks
Hands-on Vibe Coding Environments
- Leveraging Replit for collaborative AI coding
- Integrating GitHub Copilot and Qwen Coder into IDEs
- Tailoring workflows to enhance team collaboration
Code Quality and Validation in AI Workflows
- Reviewing and testing code generated by LLMs
- Safeguarding consistency, maintainability, and security
- Incorporating code validation tools into the development workflow
Enterprise Integration and Governance
- Scaling vibe coding practices across teams
- Addressing 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
- Linking 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 evaluating productivity improvements
Summary and Next Steps
Requirements
- A solid understanding of standard software development workflows
- Practical experience with Python, JavaScript, or another modern programming language
- Familiarity with Git-based version control systems
Target Audience
- Software engineers looking to explore AI-assisted development methods
- Engineering leads responsible for overseeing AI adoption in coding processes
- Enterprise development 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
