Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Understanding Code with LLMs
- Effective prompting strategies for code explanation and walkthroughs.
- Navigating unfamiliar codebases and projects.
- Analyzing control flow, dependencies, and architectural patterns.
Refactoring Code for Maintainability
- Identifying code smells, dead code, and anti-patterns.
- Restructuring functions and modules to improve clarity.
- Leveraging LLMs for naming convention suggestions and design enhancements.
Enhancing Performance and Reliability
- Utilizing AI assistance to detect inefficiencies and security vulnerabilities.
- Recommending more efficient algorithms or libraries.
- Refactoring I/O operations, database queries, and API calls.
Automating Code Documentation
- Generating function and method-level comments and summaries.
- Drafting and updating README files directly from codebases.
- Creating Swagger/OpenAPI documentation supported by LLMs.
Integration with Toolchains
- Using VS Code extensions and Copilot Labs for documentation tasks.
- Incorporating GPT or Claude into Git pre-commit hooks.
- Implementing CI pipeline integration for documentation and linting processes.
Working with Legacy and Multi-Language Codebases
- Reverse-engineering older or undocumented systems.
- Executing cross-language refactoring (e.g., migrating from Python to TypeScript).
- Reviewing case studies and participating in pair-AI programming demos.
Ethics, Quality Assurance, and Review
- Validating AI-generated changes to prevent hallucinations.
- Adopting peer review best practices 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.
- Knowledge of software architecture and code review methodologies.
- Foundational understanding of large language model functionality.
Target Audience
- Backend engineers
- DevOps teams
- Senior developers and technical leads
14 Hours
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