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Course Outline
Deciphering Code with LLMs
- Strategic prompting techniques for code explanation and guided walkthroughs
- Navigating unfamiliar codebases and project structures
- Deep-diving into control flow, dependencies, and system architecture
Refactoring for Long-Term Maintainability
- Pinpointing code smells, redundant code, and structural anti-patterns
- Reorganizing functions and modules to enhance clarity
- Leveraging LLMs to propose optimal naming conventions and design enhancements
Enhancing Performance and System Reliability
- Utilizing AI assistance to uncover inefficiencies and potential security vulnerabilities
- Recommending superior algorithms or library alternatives
- Optimizing I/O operations, database queries, and API interactions
Streamlining Code Documentation
- Auto-generating function and method-level comments and summaries
- Drafting and maintaining README files directly from the codebase
- Producing Swagger/OpenAPI documentation with LLM assistance
Seamless Toolchain Integration
- Utilizing VS Code extensions and Copilot Labs for documentation workflows
- Embedding GPT or Claude into Git pre-commit hooks
- Integrating documentation and linting checks into CI pipelines
Handling Legacy and Multi-Language Codebases
- Reverse-engineering older or undocumented systems
- Executing cross-language refactoring (e.g., migrating from Python to TypeScript)
- Case studies and demonstrations of pair-AI programming
Ethics, Quality Assurance, and Peer Review
- Validating AI-generated changes and mitigating hallucinations
- Adopting best practices for peer review when LLMs are involved
- Safeguarding reproducibility and adherence to coding standards
Summary and Future Directions
Requirements
- Practical experience with programming languages such as Python, Java, or JavaScript
- Proficiency in software architecture principles and code review procedures
- Fundamental comprehension of the operational mechanics of large language models
Target Audience
- Backend engineers
- DevOps teams
- Senior developers and technical leads
14 Hours
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