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 background of vibe coding
- The concept of “prompt-to-code” collaboration
- Differences between AI coding and traditional development
Large Language Models in Coding
- Overview of LLMs for developers: GPT-4, DeepSeek, Qwen, Mistral
- Comparison of open-source versus proprietary AI coders
- Deploying LLMs locally or via APIs
Prompt Engineering for Developers
- Effective prompting 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
- Integrating GitHub Copilot and Qwen Coder into IDEs
- Customizing workflows for team collaboration
Code Quality and Validation in AI Workflows
- Reviewing and testing code generated by LLMs
- Ensuring consistency, maintainability, and security
- Incorporating code validation tools into the workflow
Enterprise Integration and Governance
- Scaling vibe coding across teams
- 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
- Integrating 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 with both human and AI developers
- Presenting outcomes and measuring productivity improvements
Summary and Next Steps
Requirements
- Basic knowledge of software development workflows
- Experience with Python, JavaScript, or another contemporary programming language
- Familiarity with Git-based version control systems
Target Audience
- Software engineers exploring AI-assisted development
- Engineering leads overseeing AI integration in coding processes
- Enterprise teams aiming to incorporate 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
