LLMs for Code Understanding, Refactoring, and Documentation Training Course
The course 'LLMs for Code Understanding, Refactoring, and Documentation' is a technical program designed to teach participants how to apply large language models (LLMs) to enhance code quality, minimize technical debt, and automate documentation across software development teams.
This instructor-led, live training (available online or on-site) targets intermediate to advanced software professionals who want to leverage LLMs, such as 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 while improving overall code readability.
- Automate the creation and maintenance of inline comments, README files, and API documentation.
- Integrate LLM-driven insights into existing CI/CD and code review workflows.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical practice.
- Hands-on implementation within a live lab environment.
Course Customization Options
- To request a customized training version of this course, please contact us to arrange.
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
Target Audience
- Backend engineers
- DevOps teams
- Senior developers and tech leads
Open Training Courses require 5+ participants.
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NobleProg offers professional training programs designed specifically for companies and organizations. These trainings are not intended for individuals.
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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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