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.
Duration 14 hours
Course Outline
Comprehending Code through LLMs
- Strategies for prompting LLMs to explain and walk through code
- Approaching and navigating unfamiliar codebases and projects
- Examining control flow, dependencies, and system architecture
Refactoring Code for Long-Term Maintainability
- Detecting code smells, obsolete code, and architectural anti-patterns
- Reorganizing functions and modules to enhance clarity
- Utilizing LLMs to propose better naming conventions and design optimizations
Enhancing Performance and System Reliability
- Identifying inefficiencies and potential security vulnerabilities with AI support
- Recommending more efficient algorithms or alternative libraries
- Refactoring I/O operations, database queries, and API interactions
Automation of Code Documentation
- Generating function and method-level comments and summaries
- Drafting and updating README files directly from the codebase
- Producing Swagger/OpenAPI documentation with LLM assistance
Integration with Developer Toolchains
- Leveraging VS Code extensions and Copilot Labs for documentation tasks
- Embedding GPT or Claude within Git pre-commit hooks
- Integrating documentation and linting checks into CI pipelines
Managing 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 pair-AI programming demonstrations
Ethics, Quality Assurance, and Review Processes
- Validating AI-suggested changes and mitigating hallucinations
- Adopting best practices for peer review when utilizing LLMs
- Maintaining reproducibility and adherence to coding standards
Summary and Path Forward
Requirements
- Proficiency in programming languages including Python, Java, or JavaScript
- A solid grasp of software architecture principles and code review procedures
- Fundamental knowledge of the operational mechanics of large language models
Target Audience
- Backend engineers
- DevOps teams
- Senior developers and technical leads
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
