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Duration 14 hours
Course Outline
Deciphering Code with LLMs
- Advanced prompting techniques for code explanation and logical walkthroughs.
- Navigating and understanding unfamiliar codebases and project structures.
- In-depth analysis of control flow, dependencies, and system architecture.
Refactoring for Long-Term Maintainability
- Spotting code smells, obsolete code, and structural anti-patterns.
- Reorganizing functions and modules to enhance clarity and structure.
- Utilizing LLMs to propose superior naming conventions and design enhancements.
Enhancing Performance and Reliability
- Identifying performance bottlenecks and security vulnerabilities with AI support.
- Recommending more efficient algorithms or alternative library solutions.
- Optimizing I/O operations, database queries, and external API interactions.
Streamlining Code Documentation
- Generating precise function and method-level comments along with concise summaries.
- Drafting and updating README files directly from the codebase.
- Producing Swagger/OpenAPI documentation with LLM assistance.
Toolchain Integration
- Leveraging VS Code extensions and Copilot Labs for documentation workflows.
- Incorporating GPT or Claude into Git pre-commit hooks for automated checks.
- Integrating LLMs into CI pipelines for automated documentation and linting.
Handling Legacy and Multi-Language Codebases
- Reverse-engineering older systems or those lacking comprehensive documentation.
- Executing cross-language refactoring tasks (e.g., migrating from Python to TypeScript).
- Exploring case studies and live pair-AI programming demonstrations.
Ethics, Quality Assurance, and Review Processes
- Validating AI-generated modifications and mitigating the risk of hallucinations.
- Adopting best practices for peer review when utilizing LLMs.
- Ensuring reproducibility and adherence to established coding standards.
Summary and Future Directions
Requirements
- Proficiency in programming languages such as Python, Java, or JavaScript.
- Working knowledge of software architecture principles and code review methodologies.
- Fundamental comprehension of large language model mechanics.
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
