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Duration 14 hours
Course Outline
Code Analysis with LLMs
- Strategies for prompting code explanations and walkthroughs.
- Navigating unfamiliar codebases and projects.
- Evaluating control flow, dependencies, and architectural design.
Refactoring for Maintainability
- Identifying code smells, obsolete code, and anti-patterns.
- Reorganizing functions and modules for greater clarity.
- Leveraging LLMs to suggest naming conventions and design enhancements.
Enhancing Performance and Reliability
- Using AI assistance to spot inefficiencies and security vulnerabilities.
- Recommending more efficient algorithms or libraries.
- Optimizing I/O operations, database queries, and API integrations.
Streamlining Code Documentation
- Generating function and method-level comments and summaries.
- Drafting and updating README files directly from codebases.
- Creating Swagger/OpenAPI documentation with LLM support.
Toolchain Integration
- Leveraging VS Code extensions and Copilot Labs for documentation tasks.
- Embedding GPT or Claude into Git pre-commit hooks.
- Integrating documentation and linting checks into CI pipelines.
Handling Legacy and Multi-Language Codebases
- Reverse-engineering older or undocumented systems.
- Performing cross-language refactoring (e.g., migrating from Python to TypeScript).
- Demos of case studies and pair-AI programming.
Ethics, QA, and Review Processes
- Validating AI-generated changes and mitigating hallucination risks.
- Best practices for peer review when utilizing LLMs.
- Safeguarding reproducibility and adherence to coding standards.
Wrap-up and Future Directions
Requirements
- Proficiency in programming languages such as Python, Java, or JavaScript.
- A solid grasp of software architecture and code review methodologies.
- Fundamental knowledge of how large language models operate.
Target Audience
- Backend Engineers.
- DevOps Teams.
- Senior Developers and Tech 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
