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Duration 14 hours
Course Outline
Interpreting Code with LLMs
- Prompting techniques for code explanation and walkthroughs.
- Navigating unfamiliar codebases and project structures.
- Assessing control flow, dependencies, and architectural design.
Refactoring for Enhanced Maintainability
- Recognizing code smells, redundant code, and structural anti-patterns.
- Reorganizing functions and modules for improved clarity.
- Utilizing LLMs to propose naming conventions and design enhancements.
Boosting Performance and Reliability
- Identifying inefficiencies and security vulnerabilities with AI assistance.
- 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.
- Producing Swagger/OpenAPI documentation with LLM support.
Integration with Development Toolchains
- Leveraging VS Code extensions and Copilot Labs for documentation tasks.
- Integrating GPT or Claude into Git pre-commit workflows.
- Embedding documentation and linting checks into CI pipelines.
Handling Legacy and Multi-Language Codebases
- Reverse-engineering older or insufficiently documented systems.
- Cross-language refactoring scenarios (e.g., migrating from Python to TypeScript).
- Case studies and pair-AI programming demonstrations.
Ethics, Quality Assurance, and Peer Review
- Validating AI-generated modifications and mitigating hallucinations.
- Adopting best practices for peer review when incorporating LLMs.
- Safeguarding reproducibility and adherence to coding standards.
Conclusion and Future Directions
Requirements
- Proficiency in programming languages such as Python, Java, or JavaScript.
- Working knowledge of software architecture and code review methodologies.
- Fundamental understanding of the operational mechanics of Large Language Models.
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
