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Duration 14 hours
Course Outline
Code Comprehension with LLMs
- Prompt engineering strategies for code explanation and walkthroughs
- Navigating unfamiliar codebases and projects
- Analyzing control flow, dependencies, and architectural design
Refactoring for Enhanced Maintainability
- Identifying code smells, dead code, and architectural anti-patterns
- Restructuring functions and modules for greater clarity
- Leveraging LLMs to suggest improved naming conventions and design patterns
Enhancing Performance and Reliability
- Detecting inefficiencies and security vulnerabilities with AI assistance
- Recommending more efficient algorithms or libraries
- Refactoring I/O operations, database queries, and API integrations
Automating Code Documentation
- Generating function and method-level comments and summaries
- Drafting and updating README files directly from codebases
- Creating Swagger and OpenAPI documentation with LLM support
Toolchain Integration
- Utilizing VS Code extensions and Copilot Labs for documentation tasks
- Integrating GPT or Claude into Git pre-commit hooks
- Embedding documentation and linting processes into CI pipelines
Managing Legacy and Multi-Language Codebases
- Reverse-engineering older or undocumented systems
- 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
- Best practices for peer review when incorporating LLMs
- Ensuring reproducibility and adherence to coding standards
Summary and Future Directions
Requirements
- Proficiency in programming languages such as Python, Java, or JavaScript
- Knowledge of software architecture principles and code review workflows
- A foundational understanding of the operational mechanisms of large language models
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
