LLMs for Code Understanding, Refactoring, and Documentation Training Course
The course "LLMs for Code Comprehension, Refactoring, and Documentation" is a technical program designed to help teams apply large language models (LLMs) to enhance code quality, minimize technical debt, and automate documentation processes.
This instructor-led live training (available online or on-site) targets intermediate to advanced software professionals who want to leverage LLMs like GPT to better analyze, refactor, and document complex or legacy codebases.
Upon completion of this training, participants will be able to:
- Utilize LLMs to clarify code, dependencies, and logic within unfamiliar repositories.
- Identify and refactor anti-patterns to enhance code readability.
- Automatically create and maintain inline comments, README files, and API documentation.
- Integrate LLM-generated insights into existing CI/CD and code review workflows.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation in a live lab environment.
Customization Options
- To request customized training for this course, please contact us to arrange it.
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
Open Training Courses require 5+ participants.
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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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