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Duration 14 hours
Course Outline
Code Comprehension via LLMs
- Developing prompting strategies for code explanation and walkthroughs.
- Navigating unfamiliar codebases and project structures.
- Examining control flow, dependencies, and architectural design.
Refactoring for Long-Term Maintainability
- Recognizing code smells, obsolete code, and anti-patterns.
- Reorganizing functions and modules to improve clarity.
- Utilizing LLMs to propose naming conventions and design enhancements.
Enhancing Performance and System Reliability
- Identifying inefficiencies and security vulnerabilities with AI support.
- Recommending more efficient algorithms or libraries.
- Optimizing I/O operations, database queries, and API interactions.
Streamlining Code Documentation
- Automating function and method-level comments and summaries.
- Drafting and updating README files directly from existing code.
- Generating Swagger/OpenAPI documentation with LLM assistance.
Toolchain Integration
- Leveraging VS Code extensions and Copilot Labs for documentation tasks.
- Incorporating GPT or Claude into Git pre-commit hooks.
- Integrating documentation and linting processes into CI pipelines.
Handling Legacy and Polyglot Codebases
- Reverse-engineering older or undocumented systems.
- Executing cross-language refactoring (e.g., migrating from Python to TypeScript).
- Reviewing case studies and pair-AI programming demonstrations.
Ethics, Quality Assurance, and Review Processes
- Verifying AI-generated changes and mitigating hallucinations.
- Adhering to peer review best practices when utilizing LLMs.
- Safeguarding reproducibility and adherence to coding standards.
Summary and Future Directions
Requirements
- Proficiency in programming languages including Python, Java, or JavaScript.
- Knowledge of software architecture and standard code review practices.
- A foundational understanding of large language model operations.
Target Audience
- Backend engineers.
- DevOps teams.
- Senior developers and technical leads.
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
