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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
