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
Code Comprehension with LLMs
- Prompting techniques for code explanation and detailed walkthroughs.
- Navigating and working with unfamiliar codebases and projects.
- Examining control flow, dependencies, and architectural structures.
Refactoring for Enhanced Maintainability
- Identifying code smells, obsolete code, and design anti-patterns.
- Reorganizing functions and modules to improve clarity.
- Leveraging LLMs to propose naming conventions and design enhancements.
Boosting Performance and Reliability
- Identifying inefficiencies and security vulnerabilities with AI assistance.
- Recommending more efficient algorithms or libraries.
- Optimizing 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.
- Developing Swagger/OpenAPI documentation with LLM support.
Toolchain Integration
- Utilizing VS Code extensions and Copilot Labs for documentation purposes.
- Embedding GPT or Claude into Git pre-commit hooks.
- Integrating documentation and linting processes into CI pipelines.
Handling Legacy and Multi-Language Codebases
- Reverse-engineering older or poorly documented systems.
- Cross-language refactoring scenarios (e.g., migrating from Python to TypeScript).
- Case studies and pair-AI programming demonstrations.
Ethics, Quality Assurance, and Review
- Validating AI-generated modifications to prevent hallucinations.
- Best practices for peer review when utilizing LLMs.
- Maintaining reproducibility and adherence to coding standards.
Summary and Future Steps
Requirements
- Proficiency in programming languages such as Python, Java, or JavaScript.
- Working knowledge of software architecture and code review procedures.
- Fundamental understanding of large language model mechanics.
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
