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Duration 14 hours
Course Outline
Code Analysis with LLMs
- Prompting techniques for code explanation and structural walkthroughs
- Navigating unfamiliar codebases and projects
- Evaluating control flow, dependencies, and system architecture
Refactoring for Long-Term Maintainability
- Identifying code smells, obsolete code, and anti-patterns
- Restructuring functions and modules for enhanced clarity
- Leveraging LLMs for recommendations on naming conventions and design optimization
Enhancing Performance and Reliability
- Using AI assistance to detect inefficiencies and security vulnerabilities
- Recommendations for more efficient algorithms or libraries
- Optimizing I/O operations, database queries, and API calls through refactoring
Automating Technical Documentation
- Generating function and method-level comments and summaries
- Drafting and updating README files directly from codebases
- Creating Swagger/OpenAPI documentation with LLM support
Integration with Development Toolchains
- Utilizing VS Code extensions and Copilot Labs for documentation tasks
- Incorporating GPT or Claude into Git pre-commit hooks
- Integrating documentation and linting processes into CI pipelines
Managing Legacy and Multi-Language Codebases
- Reverse-engineering older or undocumented systems
- Cross-language refactoring (e.g., migrating from Python to TypeScript)
- Case studies and pair-AI programming demonstrations
Ethics, Quality Assurance, and Review Processes
- Validating AI-generated changes and mitigating hallucinations
- Best practices for peer review when utilizing LLMs
- Ensuring reproducibility and adherence to coding standards
Summary and Future Directions
Requirements
- Proficiency in programming languages such as Python, Java, or JavaScript
- Knowledge of software architecture principles and code review methodologies
- A foundational grasp of how large language models operate
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
