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
- The 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
- A developer’s overview of key LLMs: GPT-4, DeepSeek, Qwen, Mistral
- Comparing open-source versus proprietary AI coding solutions
- Deploying LLMs locally or through API integrations
Prompt Engineering for Developers
- Techniques for effective prompting to generate and refactor code
- Managing context and handling conversation states
- Building reusable prompt templates for common coding tasks
Hands-on Vibe Coding Environments
- Leveraging Replit for collaborative AI coding sessions
- Embedding GitHub Copilot and Qwen Coder into IDEs
- Tailoring workflows to support 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 development cycle
Enterprise Integration and Governance
- Scaling vibe coding practices across development teams
- Addressing AI governance, ethics, and compliance in code generation
- Creating organizational frameworks for AI-assisted development
Advanced Topics: Extending Vibe Coding
- Orchestrating multiple LLMs for hybrid AI workflows
- Connecting vibe coding with CI/CD automation systems
- Emerging trends: multi-agent development ecosystems
Team Project and Collaboration
- Developing a real-world AI-assisted coding project
- Working alongside both human and AI developers
- Presenting outcomes and quantifying productivity improvements
Summary and Next Steps
Requirements
- A solid understanding of software development processes
- Proficiency in Python, JavaScript, or another modern programming language
- Working knowledge of Git-based version control systems
Target Audience
- Software engineers investigating AI-assisted development practices
- Engineering leaders managing the adoption of AI in coding workflows
- Enterprise teams looking to embed 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
