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
Comprehending Code with LLMs
- Prompting strategies for code explanation and walkthroughs.
- Navigating unfamiliar codebases and projects.
- Analyzing control flow, dependencies, and architectural structure.
Refactoring Code for Maintainability
- Identifying code smells, dead code, and anti-patterns.
- Restructuring functions and modules to improve clarity.
- Leveraging LLMs to suggest naming conventions and design improvements.
Enhancing Performance and Reliability
- Detecting inefficiencies and security risks 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 at the function/method level.
- Writing and updating README files based on codebases.
- Creating Swagger/OpenAPI documentation with LLM support.
Integration with Development Toolchains
- Utilizing VS Code extensions and Copilot Labs for documentation tasks.
- Incorporating GPT or Claude into Git pre-commit hooks.
- Integrating documentation and linting processes into CI pipelines.
Managing Legacy and Multi-Language Codebases
- Reverse-engineering older or undocumented systems.
- Executing cross-language refactoring (e.g., transitioning from Python to TypeScript).
- Examining case studies and participating in pair-AI programming demonstrations.
Ethics, Quality Assurance, and Review
- Validating AI-generated changes and mitigating hallucinations.
- Adhering to peer review best practices when utilizing LLMs.
- Ensuring reproducibility and compliance with established coding standards.
Summary and Next Steps
Requirements
- Practical experience with programming languages such as Python, Java, or JavaScript.
- Familiarity with software architecture and code review methodologies.
- Foundational understanding of how large language models operate.
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
