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Duration 21 hours (3 days)
Course Outline
Foundations of Vibe Coding
- Origins and definition of vibe coding
- The concept of “prompt-to-code” partnership
- Distinguishing AI coding from conventional development
Utilizing Large Language Models in Coding
- LLMs for developers: An overview of GPT-4, DeepSeek, Qwen, and Mistral
- Evaluating open-source versus proprietary AI coders
- Local deployment or API-based usage of LLMs
Prompt Engineering for Developers
- Optimizing prompts for code generation and refactoring
- Managing context and conversation states
- Building reusable prompt templates for coding tasks
Practical Vibe Coding Environments
- Leveraging Replit for collaborative AI coding
- Incorporating GitHub Copilot and Qwen Coder into IDEs
- Tailoring workflows for team collaboration
Code Quality and Validation in AI Workflows
- Testing and reviewing code generated by LLMs
- Maintaining consistency, maintainability, and security
- Incorporating code validation tools into the workflow
Enterprise Integration and Governance
- Expanding vibe coding practices across teams
- Addressing AI governance, ethics, and compliance in code generation
- Creating organizational frameworks for AI-assisted development
Advanced Topics: Expanding Vibe Coding
- Merging multiple LLMs for hybrid AI workflows
- Aligning 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 assessing productivity improvements
Wrap-up and Future Steps
Requirements
- A solid grasp of software development processes
- Proficiency in Python, JavaScript, or other contemporary programming languages
- Knowledge of Git-based version control systems
Target Audience
- Software engineers interested in AI-assisted development
- Engineering leaders managing AI integration in coding practices
- 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