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Duration 14 hours
Course Outline
Interpreting Code with LLMs
- Prompt engineering strategies for code explanation and walkthroughs
- Navigating unfamiliar codebases and project structures
- Examining control flow, dependencies, and system architecture
Refactoring 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 Reliability
- Identifying inefficiencies and 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
- Generating function-level comments and method summaries
- Drafting and updating README files directly from the codebase
- Producing Swagger/OpenAPI documentation with LLM assistance
Integrating with Developer 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 systems or those lacking documentation
- Performing cross-language refactoring (e.g., migrating from Python to TypeScript)
- Case studies and pair programming demonstrations with AI
Ethics, Quality Assurance, and Code Review
- Verifying AI-generated modifications and mitigating hallucinations
- Best practices for peer review when utilizing LLMs
- Ensuring reproducibility and adherence to coding standards
Conclusion and Future Directions
Requirements
- Practical experience with programming languages like Python, Java, or JavaScript
- Knowledge of software architecture and code review procedures
- Fundamental understanding of the operational mechanics of large language models
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