LLMs for Code Understanding, Refactoring, and Documentation Training Course
The course titled "LLMs for Code Understanding, Refactoring, and Documentation" is a technical programme designed to demonstrate how large language models (LLMs) can be leveraged to enhance code quality, minimise technical debt, and automate documentation tasks within software development teams.
This instructor-led, live training session is available either online or onsite. It targets intermediate to advanced software professionals who aspire to utilise LLMs, such as GPT, to more effectively analyse, refactor, and document complex or legacy codebases.
Upon completion of this training, participants will be equipped to:
- Employ LLMs to clarify code logic, dependencies, and functionality in unfamiliar repositories.
- Recognise and refactor anti-patterns to enhance code readability.
- Automate the generation and upkeep of inline comments, README files, and API documentation.
- Seamlessly integrate LLM-derived insights into existing CI/CD and code review workflows.
Course Format
- Interactive lectures and discussions.
- Abundant exercises and practical applications.
- Hands-on implementation within a live-lab environment.
Course Customization Options
- For those interested in customising this training, please contact us to make arrangements.
Course Outline
Understanding Code with LLMs
- Prompting strategies for code explanation and walkthroughs.
- Working with unfamiliar codebases and projects.
- Analyzing control flow, dependencies, and architecture.
Refactoring Code for Maintainability
- Identifying code smells, dead code, and anti-patterns.
- Restructuring functions and modules for clarity.
- Using LLMs for suggesting naming conventions and design improvements.
Improving Performance and Reliability
- Detecting inefficiencies and security risks with AI assistance.
- Suggesting 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 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
- Experience with programming languages such as Python, Java, or JavaScript.
- Familiarity with software architecture and code review processes.
- Basic understanding of how large language models function.
Audience
- Backend engineers.
- DevOps teams.
- Senior developers and tech leads.
Need help picking the right course?
southafrica@nobleprog.co.za or +27 (0)10 005 5793
LLMs for Code Understanding, Refactoring, and Documentation Training Course - Enquiry
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
Michal Maj - XL Catlin Services SE (AXA XL)
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