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 Duration 14 hours

Course Outline

Gaining Code Insight via LLMs

  • Prompting techniques for code explanation and walkthroughs
  • Navigating unfamiliar codebases and projects
  • Evaluating control flow, dependencies, and architectural design

Refactoring for Long-Term Maintainability

  • Spotting code smells, obsolete code, and anti-patterns
  • Reorganizing functions and modules for greater clarity
  • Utilizing LLMs to propose naming conventions and design enhancements

Enhancing Performance and Stability

  • Identifying inefficiencies and security vulnerabilities with AI aid
  • Recommending more efficient algorithms or libraries
  • Optimizing I/O operations, database queries, and API calls

Streamlining Code Documentation

  • Generating comments and summaries for functions and methods
  • Drafting and refreshing README files based on codebases
  • Producing Swagger/OpenAPI documentation with LLM assistance

Seamless Toolchain Integration

  • Leveraging VS Code extensions and Copilot Labs for documentation tasks
  • Integrating GPT or Claude into Git pre-commit hooks
  • Embedding documentation and linting checks within CI pipelines

Handling Legacy and Multi-Language Systems

  • Reverse-engineering older or poorly documented systems
  • Cross-language refactoring (e.g., migrating from Python to TypeScript)
  • Case studies and pair-AI programming demonstrations

Ethics, Quality Control, and Review

  • Verifying AI-generated changes to prevent hallucinations
  • Best practices for peer review when utilizing LLMs
  • Maintaining reproducibility and adherence to coding standards

Wrap-Up and Future Directions

Requirements

  • Proficiency in programming languages like Python, Java, or JavaScript
  • Knowledge of software architecture and code review methodologies
  • A foundational grasp of large language model mechanics

Target Audience

  • Backend engineers
  • DevOps teams
  • Senior developers and technical leads

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