LLMs for Code Understanding, Refactoring, and Documentation Training Course
This technical course, 'LLMs for Code Comprehension, Refactoring, and Documentation', focuses on leveraging large language models (LLMs) to enhance code quality, mitigate technical debt, and automate documentation tasks for software development teams.
Delivered as an instructor-led, live training session (available online or onsite), this programme is designed for intermediate to advanced software professionals seeking 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 elucidate code structures, dependencies, and logic within unfamiliar repositories.
- Identify and refactor anti-patterns to enhance code readability.
- Automate the generation 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 application.
- Hands-on implementation within a live-lab environment.
Course Customisation Options
- To arrange a bespoke training session for this course, please contact us.
Course Outline
Comprehending Code with LLMs
- Prompting strategies for code explanation and walkthroughs.
- Navigating 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.
- Utilising LLMs to suggest naming conventions and design improvements.
Enhancing 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 derived from codebases.
- Creating Swagger/OpenAPI documentation with LLM support.
Integration with Toolchains
- Utilising VS Code extensions and Copilot Labs for documentation.
- Incorporating GPT or Claude into Git pre-commit hooks.
- Integrating CI pipelines for documentation and linting.
Working with Legacy and Multi-Language Codebases
- Reverse-engineering older or undocumented systems.
- Cross-language refactoring (e.g., migrating from Python to TypeScript).
- Case studies and pair-AI programming demonstrations.
Ethics, Quality Assurance, and Review
- Validating AI-generated changes and mitigating hallucinations.
- Best practices for peer review when utilising LLMs.
- Ensuring reproducibility and adherence to coding standards.
Summary and Next Steps
Requirements
- Proficiency in programming languages such as Python, Java, or JavaScript.
- Familiarity with software architecture and code review processes.
- A foundational understanding of how large language models operate.
Audience
- Backend engineers.
- DevOps teams.
- Senior developers and technical 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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