Get in Touch

Course Outline

Comprehending Code with LLMs

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

Refactoring Code for Enhanced Maintainability

  • Recognizing code smells, dead code, and anti-patterns
  • Restructuring functions and modules for improved clarity
  • Leveraging LLMs for naming convention suggestions and design improvements

Boosting Performance and Reliability

  • Detecting inefficiencies and security vulnerabilities with AI assistance
  • Recommending more efficient algorithms or libraries
  • Refactoring I/O operations, database queries, and API calls

Automating Code Documentation

  • Generating function/method-level comments and summaries
  • Writing and updating README files directly from codebases
  • Creating Swagger/OpenAPI documentation supported by LLMs

Toolchain Integration

  • Utilizing VS Code extensions and Copilot Labs for documentation
  • Incorporating GPT or Claude into Git pre-commit hooks
  • Integrating CI pipelines 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)
  • Case studies and pair-AI programming demonstrations

Ethics, Quality Assurance, and Review

  • Validating AI-generated changes and mitigating hallucinations
  • Best practices for peer review when utilizing LLMs
  • Ensuring reproducibility and adherence to coding standards

Summary and Next Steps

Requirements

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

Target Audience

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

Testimonials (1)

Upcoming Courses

Related Categories