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Duration 14 hours
Course Outline
Understanding Code with LLMs
- Prompting techniques for code explanation and walkthroughs
- Navigating unfamiliar codebases and projects
- Examining control flow, dependencies, and architecture
Refactoring Code for Maintainability
- Identifying code smells, dead code, and anti-patterns
- Reorganizing functions and modules for greater clarity
- Leveraging LLMs to propose naming conventions and design enhancements
Improving Performance and Reliability
- Detecting inefficiencies and security vulnerabilities with AI support
- Proposing more efficient algorithms or libraries
- Refactoring I/O operations, database queries, and API calls
Automating Code Documentation
- Generating function/method-level comments and summaries
- Authoring and updating README files based on codebases
- Creating Swagger/OpenAPI documentation with LLM assistance
Integration with Toolchains
- Utilizing VS Code extensions and Copilot Labs for documentation purposes
- Integrating GPT or Claude into Git pre-commit hooks
- Embedding documentation and linting processes within CI pipelines
Working with Legacy and Multi-Language Codebases
- Reverse-engineering older or undocumented systems
- Cross-language refactoring (e.g., transitioning from Python to TypeScript)
- Case studies and pair-AI programming demonstrations
Ethics, Quality Assurance, and Review
- Verifying AI-generated changes and mitigating hallucinations
- Best practices for peer review when utilizing LLMs
- Maintaining reproducibility and adherence to coding standards
Summary and Next Steps
Requirements
- Practical experience with programming languages such as Python, Java, or JavaScript.
- Knowledge of software architecture and code review procedures.
- A foundational grasp of how large language models operate.
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
