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Course Outline
Comprehending Code with LLMs
- Prompting techniques for code explanation and walkthroughs.
- Navigating unfamiliar codebases and projects.
- Analyzing control flow, dependencies, and architectural structures.
Refactoring Code for Maintainability
- Identifying code smells, dead code, and anti-patterns.
- Restructuring functions and modules to improve clarity.
- Leveraging LLMs to suggest naming conventions and design enhancements.
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 calls.
Automating Code Documentation
- Generating function/method-level comments and summaries.
- Writing and updating README files derived from codebases.
- Creating Swagger/OpenAPI documentation with LLM support.
Integration with Toolchains
- Utilizing VS Code extensions and Copilot Labs for documentation.
- Incorporating GPT or Claude into Git pre-commit hooks.
- Integrating CI pipelines for documentation and linting tasks.
Managing Legacy and Multi-Language Codebases
- Reverse-engineering older or undocumented systems.
- Executing cross-language refactoring (e.g., from Python to TypeScript).
- Reviewing case studies and pair-AI programming demonstrations.
Ethics, Quality Assurance, and Review
- Validating AI-generated changes and preventing hallucinations.
- Adhering to peer review best practices when utilizing LLMs.
- Ensuring reproducibility and compliance with coding standards.
Summary and Next Steps
Requirements
- Proficiency in programming languages such as Python, Java, or JavaScript.
- Familiarity with software architecture and code review methodologies.
- Foundational understanding of large language model operations.
Target Audience
- Backend engineers.
- DevOps teams.
- Senior developers and technical leads.
14 Hours
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
