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Course Outline
Understanding Code with LLMs
- Prompting techniques for code explanation and walkthroughs
- Navigating unfamiliar codebases and projects
- Analyzing control flow, dependencies, and architectural patterns
Refactoring for Maintainability
- Identifying code smells, dead code, and anti-patterns
- Restructuring functions and modules for improved clarity
- Leveraging LLMs to suggest naming conventions and design enhancements
Enhancing Performance and Reliability
- Identifying inefficiencies and security risks with AI support
- 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 with LLM assistance
Integration with Toolchains
- Utilizing VS Code extensions and Copilot Labs for documentation
- Incorporating GPT or Claude into Git pre-commit hooks
- Integrating documentation and linting into CI pipelines
Managing Legacy and Multi-Language Codebases
- Reverse-engineering older or undocumented systems
- Performing cross-language refactoring (e.g., Python to TypeScript)
- Exploring 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 using 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 knowledge of how large language models operate
Target Audience
- Backend engineers
- DevOps teams
- Senior developers and technical leads
14 Hours
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny