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Duration 21 hours
Course Outline
Introduction to Vibe Coding
- Origins and definition of vibe coding
- The 'prompt-to-code' collaboration mindset
- Distinguishing AI coding from traditional development methods
Large Language Models in Coding
- Developer-focused LLM overview: GPT-4, DeepSeek, Qwen, Mistral
- Analysis of open-source versus proprietary AI coders
- Deploying LLMs locally or through APIs
Prompt Engineering for Developers
- Optimizing prompts for code generation and refactoring
- Managing context and handling conversation state
- Building reusable prompt templates for coding tasks
Practical Vibe Coding Environments
- Leveraging Replit for collaborative AI coding
- Embedding GitHub Copilot and Qwen Coder into IDEs
- Tailoring workflows for enhanced team collaboration
Code Quality and Validation in AI Workflows
- Evaluating and testing code generated by LLMs
- Maintaining consistency, maintainability, and security standards
- Incorporating validation tools into the development workflow
Enterprise Integration and Governance
- Scaling vibe coding practices across teams
- Addressing AI governance, ethics, and compliance in code generation
- Establishing organizational frameworks for AI-assisted development
Advanced Topics: Expanding Vibe Coding
- Utilizing multiple LLMs for hybrid AI workflows
- Connecting vibe coding with CI/CD automation
- Emerging trends: multi-agent development ecosystems
Team Project and Collaboration
- Architecting a practical, AI-assisted coding project
- Coordinating between human and AI developers
- Presenting outcomes and assessing productivity improvements
Summary and Next Steps
Requirements
- A solid grasp of software development lifecycles
- Proficiency in Python, JavaScript, or another contemporary programming language
- Working knowledge of Git-based version control systems
Intended Audience
- Software engineers interested in AI-assisted development
- Engineering leads managing the adoption of AI in coding processes
- Enterprise teams aiming 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