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 "prompt-to-code" collaboration philosophy
- Differences between AI coding and traditional development
Large Language Models in Coding
- Overview of LLMs for developers: GPT-4, DeepSeek, Qwen, Mistral
- Comparing open-source and proprietary AI coding tools
- Deploying LLMs locally or via APIs
Prompt Engineering for Developers
- Effective prompting for code generation and refactoring
- Managing context and handling conversation state
- Developing reusable prompt templates for coding tasks
Hands-on Vibe Coding Environments
- Using Replit for collaborative AI coding
- Integrating GitHub Copilot and Qwen Coder into IDEs
- Tailoring workflows for team collaboration
Code Quality and Validation in AI Workflows
- Reviewing and testing LLM-generated code
- Maintaining consistency, maintainability, and security
- Embedding code validation tools within the workflow
Enterprise Integration and Governance
- Scaling vibe coding across teams
- Addressing AI governance, ethics, and compliance in code generation
- Establishing 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 human and AI developers
- Presenting results and evaluating productivity improvements
Summary and Next Steps
Requirements
- A solid understanding of software development workflows
- Proficiency in Python, JavaScript, or another contemporary programming language
- Knowledge of Git-based version control systems
Target Audience
- Software engineers interested in AI-assisted development
- Engineering leads overseeing AI integration in coding processes
- Enterprise development teams looking to embed 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
