LLMs for Code Understanding, Refactoring, and Documentation Training Course
This technical course explores the application of large language models (LLMs) to enhance code quality, minimize technical debt, and automate documentation processes for software teams.
Offered as an instructor-led live training (available online or onsite), this program is designed for intermediate to advanced software professionals aiming to utilize LLMs, such as GPT, to efficiently analyze, refactor, and document complex or legacy codebases.
Upon completion, participants will be capable of:
- Utilizing LLMs to clarify code logic, dependencies, and structures within unfamiliar repositories.
- Spotting and refactoring anti-patterns to enhance code readability.
- Automating the creation and maintenance of inline comments, README files, and API documentation.
- Embedding LLM-driven insights into existing CI/CD pipelines and code review workflows.
Course Format
- Interactive lectures and discussions.
- Extensive exercises and practical application.
- Hands-on implementation within a live-lab environment.
Customization Options
- For customized training arrangements, please contact us directly.
Course Outline
Comprehending Code with LLMs
- Strategies for prompting to explain and walk through code.
- Navigating unfamiliar codebases and projects.
- Analyzing control flow, dependencies, and architectural patterns.
Refactoring for Maintainability
- Identifying code smells, dead code, and anti-patterns.
- Restructuring functions and modules for improved clarity.
- Employing LLMs to suggest naming conventions and design enhancements.
Enhancing Performance and Reliability
- Detecting inefficiencies and security vulnerabilities with AI assistance.
- Recommending more efficient algorithms or libraries.
- Refactoring I/O operations, database queries, and API calls.
Automating Code Documentation
- Generating comments and summaries for functions and methods.
- Writing and updating README files derived from codebases.
- Creating Swagger/OpenAPI documentation with LLM support.
Integration with Toolchains
- Utilizing VS Code extensions and Copilot Labs for documentation.
- Incorporating GPT or Claude into Git pre-commit hooks.
- Integrating CI pipelines for automated documentation and linting.
Managing Legacy and Multi-Language Codebases
- Reverse-engineering older or undocumented systems.
- Performing cross-language refactoring (e.g., Python to TypeScript).
- Exploring case studies and pair-AI programming demonstrations.
Ethics, Quality Assurance, and Review
- Validating AI-generated changes and mitigating hallucinations.
- Adopting best practices for peer review when using 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 methodologies.
- Foundational knowledge of how large language models operate.
Target Audience
- Backend engineers.
- DevOps teams.
- Senior developers and technical leads.
Open Training Courses require 5+ participants.
LLMs for Code Understanding, Refactoring, and Documentation Training Course - Booking
LLMs for Code Understanding, Refactoring, and Documentation Training Course - Enquiry
LLMs for Code Understanding, Refactoring, and Documentation - Consultancy Enquiry
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)
Course - GitHub Copilot for Developers
Upcoming Courses
Related Courses
Advanced GitHub Copilot & AI for Projects and Infrastructure
14 HoursGitHub Copilot is an AI-powered code completion tool designed to accelerate development while enhancing quality and productivity. When combined with Artificial Intelligence applications in projects, infrastructure, and software, managers can leverage AI to optimize resource allocation, streamline workflows, and improve decision-making.
This instructor-led, live training (available online or onsite) is tailored for advanced-level managers seeking to deepen their expertise in GitHub Copilot while exploring practical AI applications in corporate environments, with examples relevant to large-scale projects and industries such as oil and gas.
By the end of this training, participants will be able to:
- Apply advanced Copilot functionalities in large-scale corporate projects.
- Integrate Copilot into multidisciplinary workflows for maximum efficiency.
- Leverage AI tools to optimize project management, infrastructure, and software acquisition.
- Implement AI-based strategies to improve planning, estimation, and time optimization.
- Recognize practical AI applications in industry-specific scenarios such as oil and gas.
Course Format
- Interactive lectures and discussions.
- Hands-on exercises and real-world case studies.
- Live lab demonstrations of AI tools and Copilot workflows.
Customisation Options for the Course
- To request a customised training session for this course, please contact us to arrange.
Advanced Cursor: Prompt Engineering, Fine-Tuning & Custom Tooling
14 HoursCursor is an advanced AI-powered development environment that enables engineers to extend, fine-tune, and customize its coding intelligence for specialized use cases and enterprise workflows.
This instructor-led, live training (available online or on-site) is designed for advanced-level developers and AI engineers who aim to design tailored prompt systems, fine-tune model behavior, and build custom extensions for internal development automation.
By the end of this training, participants will be able to:
- Design and test advanced prompt templates to achieve precise AI behavior.
- Connect Cursor to internal APIs and knowledge bases for context-aware code generation.
- Develop fine-tuned or domain-adapted AI models for specialized tasks.
- Build and deploy custom tools or adapters that securely extend Cursor’s functionality.
Course Format
- Technical presentations and guided demonstrations.
- Hands-on development and prompt optimization labs.
- Practical projects integrating Cursor with real-world enterprise systems.
Course Customization Options
- This course can be tailored to align with specific internal architectures, AI frameworks, or security compliance requirements.
Advanced GitHub Copilot
14 HoursThis instructor-led, live training in Uzbekistan (online or onsite) is aimed at advanced-level participants who wish to customize GitHub Copilot for team projects, utilize its advanced features, and integrate it seamlessly into CI/CD pipelines for enhanced collaboration and productivity.
By the end of this training, participants will be able to:
- Customize GitHub Copilot for specific project needs and team workflows.
- Leverage advanced features of Copilot for complex coding tasks.
- Integrate GitHub Copilot into CI/CD pipelines and collaborative environments.
- Optimize team collaboration using AI-powered tools.
- Manage and troubleshoot Copilot settings and permissions effectively.
GitHub Copilot: Advanced Agent Mode
21 HoursThis instructor-led, live training (Uzbekistan online or onsite) is aimed at developers who wish to use GitHub Copilot Agent Mode to autonomously build features, run tests, and manage larger coding tasks.
By the end of this training, participants will be able to activate Agent Mode, plan and iterate within the agent loop, execute terminal commands, and implement enterprise governance.
GitHub Copilot for DevOps Automation and Productivity
14 HoursGitHub Copilot is an artificial intelligence-driven coding assistant designed to automate various development tasks, including critical DevOps operations such as crafting YAML configurations, managing GitHub Actions, and developing deployment scripts.
This instructor-led live training, available both online and on-site, is tailored for beginner to intermediate professionals eager to utilize GitHub Copilot to streamline DevOps workflows, enhance automation capabilities, and significantly boost productivity.
Upon completing this training, participants will be equipped to:
- Utilize GitHub Copilot to support shell scripting, configuration management, and CI/CD pipelines.
- Harness AI-driven code completion features within YAML files and GitHub Actions.
- Accelerate processes related to testing, deployment, and automation workflows.
- Apply Copilot responsibly by understanding its limitations and adhering to industry best practices.
Course Format
- Interactive lectures and group discussions.
- Extensive exercises and practical practice sessions.
- Hands-on implementation within a live laboratory environment.
Customization Options
- For customized training arrangements, please reach out to us to coordinate your specific needs.
AI-Assisted Development & Coding with Cursor
21 HoursThis instructor-led, live training (online or onsite) is aimed at intermediate-level software developers who wish to boost productivity and code quality using AI-assisted coding with Cursor.
By the end of this training, participants will be able to:
- Install and configure Cursor for AI-assisted software development.
- Integrate Cursor with Git repositories and development workflows.
- Use natural language to generate, debug, and optimize code.
- Leverage AI capabilities for refactoring, documentation, and testing.
Cursor for Data & ML Engineering: Notebooks, Pipelines & Model Ops
14 HoursCursor is an AI-driven development environment designed to boost productivity and reliability in data and machine learning workflows through intelligent code generation, context-aware suggestions, and streamlined documentation.
This live, instructor-led training (available online or onsite) targets intermediate-level data and ML professionals looking to integrate Cursor into their daily workflows to accelerate prototyping, build scalable pipelines, and improve model operations.
Upon completion of this training, participants will be able to:
- Utilize Cursor to speed up notebook development and code exploration.
- Generate, refactor, and document ETL and feature engineering pipelines.
- Leverage AI-assisted code for model training, tuning, and evaluation.
- Enhance reproducibility, collaboration, and operational consistency in ML workflows.
Course Format
- Interactive lectures and demonstrations.
- Practical, hands-on exercises in live coding environments.
- Case studies integrating Cursor with ML pipelines and model ops tools.
Course Customization Options
- This training can be tailored to specific frameworks such as TensorFlow, PyTorch, or scikit-learn, or to organizational MLOps platforms.
Cursor Fundamentals: Accelerating Developer Productivity
14 HoursCursor is an AI-powered code editor designed to enhance developer productivity through intelligent code completions, contextual edits, and adaptive assistance.
This instructor-led, live training (online or onsite) is aimed at beginner-level developers and engineering teams who wish to streamline their coding workflow and safely leverage AI suggestions for improved efficiency.
Upon completion of this training, participants will be able to:
- Install and configure Cursor for optimal use in development projects.
- Understand and apply AI-assisted code completion, in-editor chat, and refactoring tools.
- Evaluate, accept, or modify AI-generated code suggestions effectively and securely.
- Adopt best practices for team onboarding, collaboration, and version control integration.
Format of the Course
- Interactive lecture and discussion.
- Hands-on demonstrations and guided exercises.
- Real-world coding challenges and lab practice using Cursor.
Course Customization Options
- This course can be tailored to specific programming languages or frameworks used by your team.
Cursor for Teams: Collaboration, Code Review & CI/CD Integration
14 HoursCursor is an intelligent, AI-driven development environment designed to boost team collaboration, automate code reviews, and seamlessly integrate with modern CI/CD pipelines.
This instructor-led live training, available online or on-site, targets intermediate-level technical professionals seeking to implement Cursor within their teams. The course focuses on improving collaboration efficiency, streamlining review processes, and ensuring quality standards across automated workflows.
After completing this training, participants will be equipped to:
- Configure and manage team workspaces in Cursor to support collaborative development.
- Utilize AI tools for automated code reviews, pull request creation, and merge validation.
- Establish code governance, review policies, and security guidelines using Cursor’s features.
- Connect Cursor with CI/CD systems to uphold continuous delivery and consistent quality benchmarks.
Course Format
- Instructor-led lectures combined with collaborative team discussions.
- Practical labs based on real-world team collaboration scenarios.
- Live exercises focusing on integration with CI/CD and version control tools.
Customization Options
- The curriculum can be tailored to specific CI/CD platforms, repository management tools, or enterprise security needs.
GitHub Copilot for Developers
14 HoursThis instructor-led, live training in Uzbekistan (online or onsite) is aimed at beginner-level to intermediate-level developers who wish to learn how to utilise the capabilities of GitHub Copilot effectively within modern development workflows.
GitHub Copilot in Team Environments: Collaboration Best Practices
14 HoursThis instructor-led live training in Uzbekistan (online or on-site) is designed for intermediate to advanced participants seeking to optimize team workflows, enhance collaborative coding practices, and manage Copilot usage effectively in multi-developer environments.
Upon completing this training, participants will be able to:
- Configure GitHub Copilot for team environments.
- Apply Copilot to strengthen collaborative coding practices.
- Streamline team workflows using Copilot’s capabilities.
- Oversee Copilot’s integration within multi-developer projects.
- Ensure consistent code quality and standards across all teams.
- Utilize advanced Copilot features tailored to specific team requirements.
- Integrate Copilot with other collaborative tools to boost efficiency.
Tabnine for Beginners
14 HoursThis instructor-led, live training in Uzbekistan (online or onsite) is designed for beginner developers who want to enhance their coding efficiency with Tabnine.
By the end of this training, participants will be able to:
- Install and set up Tabnine in their preferred IDE.
- Use Tabnine's autocomplete features to accelerate coding.
- Adjust Tabnine's settings for maximum assistance.
- Understand how Tabnine's AI learns from their code to offer better suggestions.
Tabnine for Advanced Developers
14 HoursThis instructor-led, live training in Uzbekistan (online or onsite) is designed for advanced-level developers and team leads who aim to master the advanced capabilities of Tabnine.
By the end of this training, participants will be able to:
- Implement Tabnine within complex software projects.
- Customize and train Tabnine's AI models for specific use cases.
- Integrate Tabnine into team workflows and development pipelines.
- Enhance code quality and accelerate development cycles by leveraging Tabnine's insights.
Tabnine: Code Smarter with AI
21 HoursThis instructor-led, live training in Uzbekistan (online or onsite) is aimed at developers ranging from novices to experts who wish to leverage AI for code generation with Tabnine.
By the end of this training, participants will be able to:
- Understand the basics of AI-powered code generation.
- Install and configure Tabnine in their development environment.
- Utilize Tabnine for efficient code completion and error correction.
- Create and train custom AI models with Tabnine for specialized tasks.
Tabnine for Python Developers
14 HoursThis instructor-led, live training in Uzbekistan (online or onsite) is aimed at intermediate-level Python developers and data scientists who wish to boost their productivity with the help of Tabnine.
By the end of this training, participants will be able to:
- Install and configure Tabnine in their Python development environment.
- Use Tabnine's autocomplete features to write Python code more efficiently.
- Customize Tabnine's behavior to fit their coding style and project needs.
- Understand how Tabnine's AI model works specifically with Python code.