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Course Outline

Mastering Code Comprehension with LLMs

  • Employing effective prompting strategies for code explanation and walkthroughs
  • Navigating unfamiliar codebases and projects with confidence
  • Analyzing control flow, dependencies, and architectural structures

Refactoring for Enhanced Maintainability

  • Identifying code smells, dead code, and common anti-patterns
  • Restructuring functions and modules to improve clarity
  • Leveraging LLMs for recommendations on naming conventions and design improvements

Boosting Performance and Reliability

  • Utilizing AI assistance to detect inefficiencies and security vulnerabilities
  • Gaining suggestions for more efficient algorithms and libraries
  • Refactoring I/O operations, database queries, and API calls for optimal performance

Automating Code Documentation

  • Generating comprehensive function/method-level comments and summaries
  • Authoring and updating README files directly from codebases
  • Creating Swagger/OpenAPI documentation with LLM support

Integrating with Development Toolchains

  • Utilizing VS Code extensions and Copilot Labs for documentation enhancements
  • Incorporating GPT or Claude into Git pre-commit hooks
  • Implementing CI pipeline integration for automated documentation and linting

Managing Legacy and Multi-Language Codebases

  • Reverse-engineering older or undocumented systems
  • Executing cross-language refactoring (e.g., migrating from Python to TypeScript)
  • Exploring case studies and pair-AI programming demonstrations

Ethics, Quality Assurance, and Review

  • Validating AI-generated changes and mitigating the risk of hallucinations
  • Adopting best practices for peer review when leveraging LLMs
  • Ensuring reproducibility and adherence to established coding standards

Summary and Future Directions

Requirements

  • Proficiency in programming languages such as Python, Java, or JavaScript
  • Familiarity with software architecture principles and code review methodologies
  • Foundational understanding of large language model mechanisms

Target Audience

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

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