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
Foundations of Vibe Coding
- Origins and definition of vibe coding
- The mindset behind "prompt-to-code" collaboration
- Distinguishing AI-assisted coding from conventional development
LLMs Applied to Coding
- Developer-focused overview of LLMs: GPT-4, DeepSeek, Qwen, Mistral
- Analysis of open-source versus proprietary AI coding tools
- Local deployment or API-based integration of LLMs
Developer-Focused Prompt Engineering
- Strategies for effective prompts in code generation and refactoring
- Managing context and conversation states
- Building reusable prompt templates for coding tasks
Practical Vibe Coding Setups
- Leveraging Replit for collaborative AI coding
- Embedding GitHub Copilot and Qwen Coder into IDEs
- Adapting workflows for team-based collaboration
Code Quality and Verification in AI Processes
- Evaluating and testing code generated by LLMs
- Safeguarding consistency, maintainability, and security
- Including code verification tools in the development cycle
Enterprise Adoption and Oversight
- Expanding vibe coding across teams
- Governing AI ethics and compliance in code creation
- Establishing organizational frameworks for AI-assisted development
Advanced Concepts: Evolving Vibe Coding
- Merging multiple LLMs for hybrid AI processes
- Aligning vibe coding with CI/CD automation
- Emerging trends: multi-agent development ecosystems
Collaborative Team Project
- Designing a practical AI-assisted coding project
- Working alongside both human and AI developers
- Sharing outcomes and evaluating productivity improvements
Conclusion and Future Directions
Requirements
- Knowledge of software development processes
- Proficiency in Python, JavaScript, or another contemporary programming language
- Proficiency with Git-based version control
Target Audience
- Software developers interested in AI-supported development
- Engineering managers supervising AI integration in coding processes
- Enterprise teams looking to embed LLMs into production environments
Testimonials (2)
The session was highly interactive and applicable to the business.
Jorge Boscan - Chevron Global Technology Services Company
Course - Advanced GitHub Copilot & AI for Projects and Infrastructure
Machine Translated
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
