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Course Outline
Comprehending Code with LLMs
- Effective prompting strategies for code explanation and walkthroughs.
- Navigating unfamiliar codebases and projects.
- Analyzing control flow, dependencies, and architectural patterns.
Refactoring Code for Enhanced Maintainability
- Spotting code smells, dead code, and anti-patterns.
- Restructuring functions and modules for improved clarity.
- Utilizing LLMs to suggest naming conventions and design enhancements.
Enhancing Performance and Reliability
- Detecting inefficiencies and security vulnerabilities with AI support.
- Recommending more efficient algorithms or libraries.
- Refactoring I/O operations, database queries, and API calls.
Automating Code Documentation
- Generating function and method-level comments and summaries.
- Writing and updating README files directly from codebases.
- Creating Swagger/OpenAPI documentation with LLM assistance.
Toolchain Integration
- Utilizing VS Code extensions and Copilot Labs for documentation purposes.
- Incorporating 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 undocumented systems.
- Performing cross-language refactoring (e.g., transitioning from Python to TypeScript).
- Exploring case studies and pair-AI programming demonstrations.
Ethics, Quality Assurance, and Review
- Validating AI-generated changes and mitigating hallucination risks.
- Adhering to best practices for peer review 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.
- Familiarity with software architecture and code review methodologies.
- Foundational knowledge of how large language models operate.
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