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Course Outline

Overview of Generative Coding Methodologies

  • Definition and historical context of generative coding approaches
  • Principles of prompt-driven software development collaboration
  • Distinctions between AI-assisted development and traditional engineering practices

Utilization of Large Language Models in Software Development

  • Overview of prominent LLMs for developer applications, including GPT-4, DeepSeek, Qwen, and Mistral
  • Comparative analysis of open-source and proprietary AI coding solutions
  • Deployment considerations: local implementation versus API integration for government environments

Prompt Engineering Strategies for Developers

  • Optimization techniques for generating and refactoring code through precise prompts
  • Management of contextual data and session state handling
  • Development of standardized prompt templates for consistent coding tasks

Practical Implementation of Generative Coding Environments

  • Leveraging platforms such as Replit for collaborative AI-driven development
  • Integration of GitHub Copilot and Qwen Coder within existing Integrated Development Environments (IDEs)
  • Customization of workflows to support team-based collaboration and efficiency

Code Quality Assurance and Validation in AI Workflows

  • Procedures for reviewing and testing code generated by Large Language Models
  • Ensuring adherence to standards regarding consistency, maintainability, and security protocols
  • Incorporation of automated validation tools into the development lifecycle

Enterprise Integration and Governance Frameworks

  • Strategies for scaling AI-assisted coding initiatives across organizational teams
  • Adherence to AI governance, ethical standards, and regulatory compliance in code generation
  • Establishment of organizational frameworks governing AI-supported software development for government entities

Advanced Applications: Expanding Generative Coding Capabilities

  • Utilization of multiple LLMs to create hybrid AI workflows
  • Integration of generative coding with Continuous Integration/Continuous Deployment (CI/CD) automation pipelines
  • Emerging trends: development ecosystems utilizing multi-agent systems

Collaborative Team Projects

  • Design and execution of a real-world AI-assisted software development project
  • Collaboration methodologies involving both human engineers and AI tools
  • Presentation of outcomes and measurement of productivity improvements

Summary and Strategic Next Steps

Requirements

  • Proficiency in software development lifecycle processes
  • Practical experience using Python, JavaScript, or comparable contemporary programming languages
  • Knowledge of version control systems utilizing Git architecture

Audience

  • Software engineers evaluating the use of AI-supported development tools
  • Engineering managers responsible for guiding AI integration within coding operations
  • Enterprise development organizations aiming to incorporate large language models into production environments for government applications
 21 Hours

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