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Course Outline
Code Comprehension via LLMs
- Prompting techniques for code explanation and walkthroughs.
- Navigation and analysis of unfamiliar codebases and projects.
- Examination of control flow, dependencies, and architectural patterns.
Code Refactoring for Maintainability
- Identification of code smells, dead code, and anti-patterns.
- Restructuring of functions and modules to improve clarity.
- Application of LLMs for recommendations on naming conventions and design enhancements.
Enhancing Performance and Reliability
- Detection of inefficiencies and security vulnerabilities using AI assistance.
- Proposal of more efficient algorithms or libraries.
- Refactoring of I/O operations, database queries, and API calls.
Automating Code Documentation
- Generation of function/method-level comments and summaries.
- Creation and updates of README files derived from codebases.
- Development of Swagger/OpenAPI documentation with LLM support.
Toolchain Integration
- Leveraging VS Code extensions and Copilot Labs for documentation tasks.
- Incorporating GPT or Claude into Git pre-commit hooks.
- Integrating CI pipelines for automated documentation and linting.
Managing Legacy and Multi-Language Codebases
- Reverse-engineering older or undocumented systems.
- Cross-language refactoring (e.g., transitioning from Python to TypeScript).
- Exploration of case studies and pair-AI programming demonstrations.
Ethics, Quality Assurance, and Review
- Verification of AI-generated changes and prevention of hallucinations.
- Best practices for peer review when utilizing LLMs.
- Ensuring reproducibility and adherence to coding standards.
Summary and Future Steps
Requirements
- Proficiency in programming languages such as Python, Java, or JavaScript.
- Knowledge of software architecture and code review methodologies.
- Fundamental understanding of how large language models operate.
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