Kursplan

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

Krav

  • 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 timmar

Antal deltagare


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