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Course Outline

Understanding Code with LLMs

  • Prompting strategies for code explanation and walkthroughs.
  • Working with unfamiliar codebases and projects.
  • Analyzing control flow, dependencies, and architecture.

Refactoring Code for Maintainability

  • Identifying code smells, dead code, and anti-patterns.
  • Restructuring functions and modules for clarity.
  • Using LLMs for suggesting naming conventions and design improvements.

Improving Performance and Reliability

  • Detecting inefficiencies and security risks with AI assistance.
  • Suggesting 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 from codebases.
  • Creating Swagger/OpenAPI docs with LLM support.

Integration with Toolchains

  • Using VS Code extensions and Copilot Labs for documentation.
  • Incorporating GPT or Claude in Git pre-commit hooks.
  • CI pipeline integration for documentation and linting.

Working with Legacy and Multi-Language Codebases

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

Ethics, Quality Assurance, and Review

  • Validating AI-generated changes and avoiding hallucinations.
  • Peer review best practices when using LLMs.
  • Ensuring reproducibility and compliance with coding standards.

Summary and Next Steps

Requirements

  • Experience with programming languages such as Python, Java, or JavaScript.
  • Familiarity with software architecture and code review processes.
  • Basic understanding of how large language models function.

Audience

  • Backend engineers.
  • DevOps teams.
  • Senior developers and tech leads.
 14 Hours

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