Plan du cours

Introduction to Vibe Coding

  • Definition and history of vibe coding
  • Philosophy of “prompt-to-code” collaboration
  • How AI coding differs from traditional development

Large Language Models in Coding

  • Overview of LLMs for developers: GPT-4, DeepSeek, Qwen, Mistral
  • Comparing open-source vs. proprietary AI coders
  • Deploying LLMs locally or via APIs

Prompt Engineering for Developers

  • Effective prompting for generating and refactoring code
  • Context management and conversation state handling
  • Creating reusable prompt templates for coding tasks

Hands-on Vibe Coding Environments

  • Using Replit for collaborative AI coding
  • Integrating GitHub Copilot and Qwen Coder into IDEs
  • Customizing workflows for team collaboration

Code Quality and Validation in AI Workflows

  • Reviewing and testing LLM-generated code
  • Ensuring consistency, maintainability, and security
  • Integrating code validation tools in the workflow

Enterprise Integration and Governance

  • Scaling vibe coding across teams
  • AI governance, ethics, and compliance in code generation
  • Designing 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
  • Future trends: multi-agent development ecosystems

Team Project and Collaboration

  • Designing a real-world AI-assisted coding project
  • Collaborating with human and AI developers
  • Presenting results and measuring productivity gains

Summary and Next Steps

Pré requis

  • An understanding of software development workflows
  • Experience with Python, JavaScript, or another modern programming language
  • Familiarity with Git-based version control systems

Audience

  • Software engineers exploring AI-assisted development
  • Engineering leads overseeing AI adoption in coding workflows
  • Enterprise development teams seeking to integrate LLMs into production pipelines
 21 Heures

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