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Course Outline
Code Comprehension via LLMs
- Prompting strategies for detailed code explanation and walkthroughs.
- Navigating unfamiliar codebases and project structures.
- Analyzing control flow, dependencies, and architectural components.
Refactoring for Long-Term Maintainability
- Identifying code smells, dead code, and structural anti-patterns.
- Restructuring functions and modules to enhance clarity.
- Leveraging LLMs to suggest optimal naming conventions and design improvements.
Enhancing Performance and Reliability
- Detecting inefficiencies and potential security risks with AI assistance.
- Proposing more efficient algorithms or alternative libraries.
- Refactoring I/O operations, database queries, and API call structures.
Streamlining Code Documentation
- Generating function and method-level comments and summaries.
- Drafting and updating README files directly from codebases.
- Creating Swagger/OpenAPI documentation with LLM support.
Toolchain Integration
- Utilizing VS Code extensions and Copilot Labs for documentation workflows.
- Incorporating GPT or Claude into Git pre-commit hooks.
- Integrating documentation generation and linting into CI pipelines.
Handling Legacy and Multi-Language Codebases
- Reverse-engineering older systems or those lacking documentation.
- 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 hallucination risks.
- Establishing peer review best practices when integrating LLMs.
- Ensuring reproducibility and adherence to coding standards.
Summary and Future Directions
Requirements
- Proficiency in programming languages such as Python, Java, or JavaScript.
- Familiarity with software architecture principles and standard code review processes.
- A foundational understanding of the operational mechanics of large language models.
Target Audience
- Backend Engineers
- DevOps Teams
- Senior Developers and Tech 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
