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Course Outline

Leveraging Large Language Models for Code Comprehension

  • Strategic prompting techniques to facilitate code explanation and step-by-step walkthroughs
  • Techniques for navigating unfamiliar codebases and complex project structures
  • Analysis of control flow, interdependencies, and overall system architecture

Refactoring for Enhanced Maintainability

  • Identification of code smells, obsolete logic, and established anti-patterns
  • Reorganization of functions and modules to improve clarity and structure
  • Utilization of LLMs to recommend naming conventions and design enhancements

Optimizing Performance and Reliability

  • Detection of inefficiencies and potential security vulnerabilities through AI-assisted analysis
  • Recommendations for more efficient algorithms or updated libraries
  • Refactoring of input/output operations, database queries, and API interactions

Automating Documentation Generation

  • Production of granular comments and summaries for individual functions and methods
  • Creation and maintenance of README files derived directly from codebases
  • Generation of Swagger/OpenAPI specifications with LLM assistance

Integration into Development Toolchains

  • Utilization of VS Code extensions and Copilot Labs to support documentation workflows
  • Incorporation of GPT or Claude models within Git pre-commit hooks
  • Implementation of CI pipeline stages for automated documentation and linting

Managing Legacy and Multi-Language Environments

  • Reverse engineering approaches for legacy or undocumented systems
  • Cross-language refactoring efforts, such as translating from Python to TypeScript
  • Case studies and demonstrations of pair-AI programming methodologies

Ethical Considerations, Quality Assurance, and Review

  • Validation of AI-generated modifications and mitigation of hallucination risks
  • Establishment of peer review best practices for LLM-assisted development
  • Maintenance of reproducibility and adherence to established coding standards

Summary and Next Steps

Requirements

  • Proficiency in programming languages including Python, Java, or JavaScript
  • Knowledge of software architecture principles and code review procedures
  • Fundamental comprehension of large language model operations

Target Audience

  • Backend engineering staff
  • DevOps specialists
  • Senior developers and technical leads
 14 Hours

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