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Duration 14 hours
Course Outline
Code Comprehension via LLMs
- Effective prompting strategies for code explanation and logical walkthroughs
- Navigating unfamiliar codebases and project structures
- Analyzing control flow, dependencies, and overall architecture
Refactoring for Long-Term Maintainability
- Identifying code smells, dead code, and structural anti-patterns
- Restructuring functions and modules for enhanced clarity
- Leveraging LLMs to suggest improved naming conventions and design patterns
Enhancing Performance and System Reliability
- Detecting inefficiencies and potential security risks with AI assistance
- Optimizing algorithm choices and library selections
- Refactoring I/O operations, database queries, and API interactions
Streamlining Code Documentation
- Generating comprehensive function/method-level comments and summaries
- Creating and updating README files directly from codebases
- Developing Swagger/OpenAPI documentation with LLM support
Toolchain Integration
- Utilizing VS Code extensions and Copilot Labs for documentation tasks
- Incorporating GPT or Claude into Git pre-commit hooks
- Integrating LLMs into CI pipelines for automated documentation and linting
Managing Legacy and Multi-Language Codebases
- Reverse-engineering older or poorly documented systems
- Executing cross-language refactoring (e.g., migrating from Python to TypeScript)
- Case studies and demonstrations of pair-AI programming techniques
Ethics, Quality Assurance, and Review Processes
- Validating AI-generated changes and mitigating hallucination risks
- Best practices for peer review when utilizing LLMs
- Ensuring reproducibility and adherence to coding standards
Wrap-Up and Future Directions
Requirements
- Proficiency in programming languages such as Python, Java, or JavaScript
- A solid grasp of software architecture and standard code review processes
- A foundational understanding 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
