LLMs for Code Understanding, Refactoring, and Documentation Training Course
This technical curriculum addresses the application of large language models (LLMs) to enhance code comprehension, facilitate refactoring, and streamline documentation processes. The program is designed to help software engineering teams improve code quality, mitigate technical debt, and automate documentation efforts for government and public sector needs.
Delivered as instructor-led live training available in online or onsite formats, this course targets intermediate to advanced software professionals seeking to leverage LLM technologies, such as GPT, to analyze, refactor, and document complex or legacy code repositories more efficiently.
Upon completion of this training, participants will be capable of:
- Utilizing LLMs to clarify code structure, dependencies, and logic within unfamiliar repository environments.
- Identifying anti-patterns and executing refactoring tasks to enhance code readability and maintainability.
- Automating the generation and maintenance of inline comments, README documentation, and API references.
- Integrating LLM-derived insights into existing continuous integration and continuous deployment (CI/CD) pipelines and code review processes.
Course Format
- Interactive lectures and group discussions.
- Extensive practical exercises and guided practice.
- Hands-on implementation within a live laboratory environment.
Customization Options
- To arrange customized training tailored to specific organizational requirements, please contact us directly.
Course Outline
Leveraging Large Language Models for Code Comprehension
- Strategic prompting techniques to facilitate code explanation and step-by-step walkthroughs
- Techniques for navigating unfamiliar codebases and complex project structures
- Analysis of control flow, interdependencies, and overall system architecture
Refactoring for Enhanced Maintainability
- Identification of code smells, obsolete logic, and established anti-patterns
- Reorganization of functions and modules to improve clarity and structure
- Utilization of LLMs to recommend naming conventions and design enhancements
Optimizing Performance and Reliability
- Detection of inefficiencies and potential security vulnerabilities through AI-assisted analysis
- Recommendations for more efficient algorithms or updated libraries
- Refactoring of input/output operations, database queries, and API interactions
Automating Documentation Generation
- Production of granular comments and summaries for individual functions and methods
- Creation and maintenance of README files derived directly from codebases
- Generation of Swagger/OpenAPI specifications with LLM assistance
Integration into Development Toolchains
- Utilization of VS Code extensions and Copilot Labs to support documentation workflows
- Incorporation of GPT or Claude models within Git pre-commit hooks
- Implementation of CI pipeline stages for automated documentation and linting
Managing Legacy and Multi-Language Environments
- Reverse engineering approaches for legacy or undocumented systems
- Cross-language refactoring efforts, such as translating from Python to TypeScript
- Case studies and demonstrations of pair-AI programming methodologies
Ethical Considerations, Quality Assurance, and Review
- Validation of AI-generated modifications and mitigation of hallucination risks
- Establishment of peer review best practices for LLM-assisted development
- Maintenance of reproducibility and adherence to established coding standards
Summary and Next Steps
Requirements
- Proficiency in programming languages including Python, Java, or JavaScript
- Knowledge of software architecture principles and code review procedures
- Fundamental comprehension of large language model operations
Target Audience
- Backend engineering staff
- DevOps specialists
- Senior developers and technical leads
Runs with a minimum of 4 + people. For 1-to-1 or private group training, request a quote.
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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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