LLMs for Code Understanding, Refactoring, and Documentation

• 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

LLMs for Code Understanding, Refactoring, and Documentation is a technical course focused on applying large language models (LLMs) to improve code quality, reduce technical debt, and automate documentation tasks across software teams.

This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level software professionals who wish to use LLMs such as GPT to analyze, refactor, and document complex or legacy codebases more effectively.

By the end of this training, participants will be able to:

• Use LLMs to explain code, dependencies, and logic in unfamiliar repositories.
• Identify and refactor anti-patterns and improve code readability.
• Automatically generate and maintain in-line comments, README files, and API documentation.
• Integrate LLM-driven insights into existing CI/CD and review workflows.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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