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Course Outline
Utilizing Large Language Models for Code Comprehension
- Strategic prompting techniques for code analysis and step-by-step walkthroughs
- Evaluating unfamiliar codebases and development projects
- Assessing control flow, dependency structures, and architectural patterns
Refactoring Code to Enhance Maintainability
- Detecting code smells, obsolete logic, and anti-patterns
- Redefining functions and modules to improve clarity
- Leveraging LLMs to recommend naming conventions and design enhancements
Enhancing System Performance and Reliability
- Identifying inefficiencies and security vulnerabilities using AI assistance
- Evaluating more efficient algorithms and software libraries
- Optimizing I/O operations, database queries, and API interactions
Automating Code Documentation
- Generating function-level comments and executive summaries
- Drafting and maintaining README files derived from codebases
- Developing Swagger/OpenAPI specifications with LLM support
Integration with Development Toolchains
- Employing VS Code extensions and Copilot Labs for documentation tasks
- Implementing GPT or Claude within Git pre-commit workflows
- Integrating documentation and linting processes into CI pipelines
Managing Legacy and Polyglot Codebases
- Reverse-engineering older or poorly documented systems
- Facilitating cross-language refactoring (e.g., migrating from Python to TypeScript)
- Presenting case studies and paired AI programming demonstrations
Ethical Considerations, Quality Assurance, and Review
- Validating AI-generated modifications to mitigate hallucinations
- Establishing peer review best practices for LLM-assisted development
- Ensuring reproducibility and adherence to established coding standards
Summary and Next Steps
Requirements
**Technical Competencies**
* Proficiency in development environments utilizing Python, Java, or JavaScript.
* Demonstrated knowledge of software architectural principles and established code review protocols.
* Foundational comprehension of large language model operations and applications for government initiatives.
**Intended Audience**
* Backend engineering personnel.
* DevOps operational teams.
* Senior technical developers and engineering leadership.
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
