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
Analyzing Source Code with Large Language Models
- Developing effective prompting techniques for code analysis and walkthroughs
- Navigating unfamiliar repositories and project structures
- Evaluating control logic, inter-component dependencies, and system architecture
Optimizing Code Structure for Long-Term Maintainability
- Detecting code vulnerabilities, obsolete segments, and design flaws
- Reorganizing functions and modules to enhance clarity
- Leveraging LLMs to propose standardized naming conventions and architectural enhancements
Enhancing System Performance and Operational Reliability
- Identifying performance bottlenecks and security exposures with AI assistance
- Recommending optimized algorithms or library selections
- Improving I/O handling, database interactions, and external API calls
Streamlining Technical Documentation Processes
- Generating detailed function-level comments and functional summaries
- Authoring and maintaining README files based on existing codebases
- Developing Swagger and OpenAPI specifications with LLM support
Integrating AI Tools into Development Workflows
- Utilizing VS Code extensions and Copilot Labs to assist with documentation
- Incorporating GPT or Claude into Git pre-commit validation hooks
- Embedding AI-driven checks into CI pipelines for documentation accuracy and linting
Managing Legacy Systems and Multi-Language Environments
- Reverse-engineering older or insufficiently documented systems
- Executing cross-language refactoring tasks (e.g., migrating from Python to TypeScript)
- Reviewing case studies and paired AI programming demonstrations
Ensuring Ethical Standards, Quality Assurance, and Governance
- Validating AI-generated modifications and mitigating hallucination risks
- Adopting peer review best practices when utilizing LLMs
- Safeguarding reproducibility and adherence to established coding standards
Summary and Recommended Path Forward
Requirements
- Demonstrated proficiency in programming languages such as Python, Java, or JavaScript
- Working knowledge of software architecture and standard code review processes
- Foundational understanding of large language model capabilities and limitations
Intended Audience
- Backend engineering personnel
- 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
