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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
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