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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

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