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Duration 14 hours
Course Outline
Interpreting Code with LLMs
- Prompt engineering techniques for code explanation and walkthroughs
- Navigating unfamiliar codebases and project structures
- Examining control flow, dependencies, and system architecture
Refactoring for Long-Term Maintainability
- Pinpointing code smells, obsolete code, and architectural anti-patterns
- Restructuring functions and modules to enhance clarity
- Leveraging LLMs to propose better naming conventions and design enhancements
Optimising Performance and Reliability
- Identifying inefficiencies and security vulnerabilities with AI support
- Recommending more efficient algorithms or library alternatives
- Refactoring I/O operations, database queries, and API interactions
Streamlining Code Documentation
- Generating function and method-level comments and summaries
- Drafting and updating README files directly from codebases
- Producing Swagger/OpenAPI documentation with LLM assistance
Integration with Developer Toolchains
- Utilising VS Code extensions and Copilot Labs for documentation tasks
- Embedding GPT or Claude within Git pre-commit hooks
- Integrating LLMs into CI pipelines for automated documentation and linting
Managing Legacy and Multi-Language Codebases
- Reverse-engineering legacy or undocumented systems
- Cross-language refactoring (e.g., migrating from Python to TypeScript)
- Case studies and pair-AI programming demonstrations
Ethics, Quality Assurance, and Code Review
- Verifying AI-generated changes to mitigate hallucinations
- Best practices for peer review when LLMs are involved
- Maintaining reproducibility and adherence to coding standards
Conclusion and Future Pathways
Requirements
- Proficiency in programming languages such as Python, Java, or JavaScript
- Knowledge of software architecture and standard code review protocols
- A foundational grasp of large language model mechanics
Target Audience
- Backend Engineers
- DevOps Teams
- Senior Developers and Technical Leads
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
