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 Duration 21 hours (3 days)

Course Outline

Introduction to Vibe Coding

  • Origins and definition of vibe coding
  • The concept of “prompt-to-code” collaboration
  • Differences between AI coding and traditional development

Large Language Models in Coding

  • Overview of developer-focused LLMs: GPT-4, DeepSeek, Qwen, Mistral
  • Comparison of open-source versus proprietary AI coding tools
  • Deploying LLMs locally or through APIs

Prompt Engineering for Developers

  • Effective prompting techniques for code generation and refactoring
  • Managing context and handling conversation state
  • Building reusable prompt templates for coding tasks

Hands-on Vibe Coding Environments

  • Leveraging 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 code generated by LLMs
  • Maintaining consistency, maintainability, and security
  • Incorporating code validation tools into the workflow

Enterprise Integration and Governance

  • Scaling vibe coding practices across teams
  • Addressing AI governance, ethics, and compliance in code generation
  • Creating 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
  • Emerging trends: multi-agent development ecosystems

Team Project and Collaboration

  • Planning a real-world AI-assisted coding project
  • Working alongside human and AI developers
  • Presenting outcomes and assessing productivity improvements

Summary and Next Steps

Requirements

  • Knowledge of software development processes
  • Proficiency in Python, JavaScript, or other modern programming languages
  • Experience with Git-based version control systems

Target Audience

  • Software engineers interested in AI-assisted development
  • Engineering managers supervising AI adoption in coding workflows
  • Enterprise development teams aiming to integrate LLMs into production pipelines

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