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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

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