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Duration 14 hours
Course Outline
Understanding Code with LLMs
- Prompting strategies for code explanation and walkthroughs.
- Navigating unfamiliar codebases and project structures.
- Analyzing control flow, dependencies, and system architecture.
Refactoring Code for Maintainability
- Identifying code smells, dead code, and anti-patterns.
- Restructuring functions and modules for enhanced clarity.
- Utilizing LLMs to propose naming conventions and design improvements.
Improving Performance and Reliability
- Detecting inefficiencies and security risks with AI assistance.
- Proposing more efficient algorithms or libraries.
- Refactoring I/O operations, database queries, and API calls.
Automating Code Documentation
- Generating function/method-level comments and summaries.
- Writing and updating README files derived from codebases.
- Creating Swagger/OpenAPI docs with LLM support.
Integration with Toolchains
- Using VS Code extensions and Copilot Labs for documentation.
- Incorporating GPT or Claude in Git pre-commit hooks.
- CI pipeline integration for documentation and linting.
Working with Legacy and Multi-Language Codebases
- Reverse-engineering older or undocumented systems.
- Cross-language refactoring (e.g., from Python to TypeScript).
- Case studies and pair-AI programming demos.
Ethics, Quality Assurance, and Review
- Validating AI-generated changes and avoiding hallucinations.
- Peer review best practices when using LLMs.
- Ensuring reproducibility and compliance with coding standards.
Summary and Next Steps
Requirements
- Proficiency in programming languages such as Python, Java, or JavaScript.
- Knowledge of software architecture principles and code review methodologies.
- 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
