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
Decoding Code with LLMs
- Prompting techniques for code explanation and walkthroughs
- Navigating unfamiliar codebases and projects
- Analyzing control flow, dependencies, and architectural design
Refactoring for Enhanced Maintainability
- Identifying code smells, dead code, and structural anti-patterns
- Restructuring functions and modules for improved clarity
- Utilizing LLMs to propose naming conventions and design optimizations
Boosting Performance and Reliability
- Detecting 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/method-level comments and summaries
- Authoring and updating README files directly from codebases
- Creating Swagger/OpenAPI documentation with LLM support
Toolchain Integration
- 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
- Cross-language refactoring (e.g., migrating from Python to TypeScript)
- Case studies and pair-AI programming demonstrations
Ethics, Quality Assurance, and Review
- Validating AI-generated changes and mitigating hallucinations
- Best practices for peer review when incorporating 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 principles and code review methodologies
- Fundamental understanding of large language model functionalities
Target Audience
- Backend Engineers
- DevOps Teams
- Senior Developers and Tech 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
