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Duration 14 hours
Course Outline
Understanding Code with LLMs
- Strategies for prompting code explanation and detailed walkthroughs
- Navigating unfamiliar codebases and project structures
- Analyzing control flow, dependencies, and system architecture
Refactoring Code for Maintainability
- Identifying code smells, dead code, and architectural anti-patterns
- Restructuring functions and modules for improved clarity
- Leveraging LLMs to propose naming conventions and design enhancements
Improving Performance and Reliability
- Detecting inefficiencies and security vulnerabilities with AI assistance
- Optimizing algorithms and selecting appropriate libraries
- Refactoring I/O operations, database queries, and API interactions
Automating Code Documentation
- Generating function-level comments and method summaries
- Creating and updating README files directly from codebases
- Developing Swagger/OpenAPI documentation with LLM support
Integration with Toolchains
- Utilizing VS Code extensions and Copilot Labs for documentation tasks
- Integrating GPT or Claude into Git pre-commit hooks
- Incorporating LLMs into CI pipelines for documentation and linting
Working with Legacy and Multi-Language Codebases
- Reverse-engineering older or poorly documented systems
- Cross-language refactoring scenarios (e.g., migrating from Python to TypeScript)
- Case studies and pair-AI programming demonstrations
Ethics, Quality Assurance, and Review
- Validating AI-generated changes and mitigating hallucination risks
- Best practices for peer review when utilizing LLMs
- Ensuring reproducibility and adherence to coding standards
Summary and Next Steps
Requirements
- Practical experience with programming languages such as Python, Java, or JavaScript
- Proficiency in software architecture principles and code review methodologies
- Fundamental comprehension 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
