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Duration 14 hours
Course Outline
Deciphering Code with LLMs
- Effective prompting techniques for explaining code and guiding walkthroughs
- Navigating unfamiliar repositories and project structures
- Examining control flow, interdependencies, and overall architecture
Refactoring for Long-Term Maintainability
- Spotting code smells, obsolete code, and common anti-patterns
- Reorganizing functions and modules to enhance clarity
- Leveraging LLMs to propose better naming conventions and design optimizations
Enhancing Performance and System Reliability
- Identifying inefficiencies and potential security vulnerabilities with AI support
- Recommending more efficient algorithms or third-party libraries
- Optimizing I/O operations, database queries, and API interactions
Streamlining Code Documentation
- Producing high-quality comments and summaries at the function and method level
- Drafting and refreshing README files directly from existing codebases
- Generating Swagger/OpenAPI specifications with LLM assistance
Seamless Integration with Development Toolchains
- Utilizing VS Code extensions and Copilot Labs to manage documentation
- Embedding GPT or Claude into Git pre-commit hooks
- Integrating LLM capabilities into CI pipelines for documentation and linting
Managing Legacy and Polyglot Codebases
- Reverse-engineering outdated or poorly documented systems
- Performing cross-language refactoring (e.g., migrating from Python to TypeScript)
- Exploring case studies and pair-AI programming demonstrations
Ethical Considerations, Quality Assurance, and Review
- Verifying AI-generated changes and mitigating hallucinations
- Adhering to peer review best practices when incorporating LLMs
- Maintaining reproducibility and ensuring compliance with established coding standards
Wrap-Up and Future Directions
Requirements
- Proficiency in programming languages including Python, Java, or JavaScript
- Working knowledge of software architecture and code review procedures
- Fundamental comprehension of large language model mechanics
Target Audience
- Backend engineers
- DevOps teams
- Senior developers and tech 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
