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Duration 14 hours
Course Outline
Comprehending Code via LLMs
- Prompting techniques for code explanation and detailed walkthroughs
- Navigating unfamiliar codebases and project structures
- Analyzing control flow, dependency maps, and architectural patterns
Refactoring Code for Long-Term Maintainability
- Identifying code smells, redundant code, and structural anti-patterns
- Reorganising functions and modules to enhance clarity
- Leveraging LLMs to propose naming conventions and design improvements
Enhancing Performance and Reliability
- Detecting inefficiencies and potential security risks with AI assistance
- Recommending more efficient algorithms or library alternatives
- Optimising I/O operations, database queries, and API calls
Automating Technical Documentation
- Generating function- and method-level comments and summaries
- Drafting and updating README files directly from codebases
- Creating Swagger/OpenAPI documentation with LLM support
Integrating with Development Toolchains
- Utilising VS Code extensions and Copilot Labs for documentation generation
- Embedding GPT or Claude into Git pre-commit hooks
- Integrating documentation and linting processes into CI pipelines
Managing Legacy and Multi-Language Codebases
- Reverse-engineering older or poorly documented systems
- Performing cross-language refactoring (e.g., migrating from Python to TypeScript)
- Reviewing case studies and pair-AI programming demonstrations
Ethics, Quality Assurance, and Review Processes
- Validating AI-generated changes and mitigating hallucination risks
- Adopting peer review best practices when using 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 code review processes
- A foundational understanding of how large language models operate
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
