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Duration 14 hours
Course Outline
Code Analysis with LLMs
- Strategies for prompting code explanations and walkthroughs
- Navigating unfamiliar codebases and projects
- Analyzing control flow, dependencies, and architectural design
Refactoring for Maintainability
- Identifying code smells, dead code, and anti-patterns
- Restructuring functions and modules for enhanced clarity
- Leveraging LLMs for naming conventions and design optimization suggestions
Enhancing Performance and Reliability
- Using AI assistance to detect inefficiencies and security risks
- Suggesting more efficient algorithms or library choices
- Refactoring I/O operations, database queries, and API interactions
Automating Documentation
- Generating function and method-level comments and summaries
- Drafting and updating README files based on codebases
- Generating Swagger/OpenAPI documentation with LLM support
Toolchain Integration
- Utilizing VS Code extensions and Copilot Labs for documentation
- Incorporating GPT or Claude into Git pre-commit hooks
- Integrating LLMs into CI pipelines for documentation and linting
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
- Validating AI-generated changes and mitigating hallucinations
- Best practices for peer review when utilizing LLMs
- Safeguarding reproducibility and adherence to coding standards
Summary and Next Steps
Requirements
- Proficiency in programming languages such as Python, Java, or JavaScript
- Working knowledge of software architecture and code review methodologies
- Fundamental understanding of large language model mechanics
Audience
- Backend engineers
- DevOps teams
- Senior developers and tech leads
Testimonials (2)
The session was highly interactive and applicable to the business.
Jorge Boscan - Chevron Global Technology Services Company
Course - Advanced GitHub Copilot & AI for Projects and Infrastructure
Machine Translated
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