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Duration 14 hours
Course Outline
Comprehending Code through LLMs
- Developing prompting strategies for code explanation and walkthroughs
- Navigating unfamiliar codebases and project structures
- Examining control flow, dependencies, and system architecture
Refactoring Code for Long-Term Maintainability
- Recognizing code smells, obsolete code, and anti-patterns
- Reorganizing functions and modules for greater clarity
- Leveraging LLMs to propose naming conventions and design enhancements
Enhancing Performance and System Reliability
- Identifying inefficiencies and potential security risks with AI assistance
- Recommending more efficient algorithms or alternative libraries
- Optimizing I/O operations, database queries, and external API calls
Streamlining Code Documentation
- Generating comments and summaries at the function and method level
- Drafting and updating README files directly from codebases
- Producing Swagger/OpenAPI documentation with LLM support
Integration with Development Toolchains
- Utilizing VS Code extensions and Copilot Labs for documentation tasks
- Embedding GPT or Claude into Git pre-commit hooks
- Integrating LLMs into CI pipelines for documentation generation and linting
Managing Legacy and Multi-Language Codebases
- Reverse-engineering older or poorly documented systems
- Executing cross-language refactoring tasks (e.g., migrating from Python to TypeScript)
- Reviewing case studies and pair-AI programming demonstrations
Ethics, Quality Assurance, and Review Processes
- Verifying AI-generated changes and mitigating the risk of hallucinations
- Adhering to peer review best practices when utilizing LLMs
- Maintaining reproducibility and compliance with established coding standards
Conclusion and Future Directions
Requirements
- Proficiency in programming languages such as Python, Java, or JavaScript
- Knowledge of software architecture principles and code review procedures
- Fundamental understanding of the operational mechanics of large language models
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
