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
Mastering Code Comprehension with LLMs
- Effective prompting strategies for code explanation and walkthroughs.
- Techniques for navigating unfamiliar codebases and projects.
- Analyzing control flow, dependencies, and system architecture.
Refactoring Code for Enhanced Maintainability
- Identifying code smells, dead code, and anti-patterns.
- Restructuring functions and modules to improve clarity.
- Utilizing LLMs to suggest naming conventions and design enhancements.
Boosting 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 function/method-level comments and summaries.
- Drafting and updating README files directly from codebases.
- Creating Swagger/OpenAPI documentation with LLM support.
Integration with Development 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.
Navigating Legacy and Multi-Language Codebases
- Reverse-engineering older or undocumented systems.
- Performing cross-language refactoring (e.g., migrating from Python to TypeScript).
- Engaging in case studies and pair-AI programming demonstrations.
Ethics, Quality Assurance, and Review
- Validating AI-generated changes and mitigating hallucinations.
- Establishing best practices for peer reviews when utilizing 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.
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