AI-Augmented Development with Coding Agents
HOT COURSE
Certificate of Completion by Code.Hub
This hands-on course develops practical skills in working with AI coding agents as structured development partners. Participants learn how to guide, refine, and critically evaluate AI-generated code throughout a complete project lifecycle-from requirements definition to feature implementation, refactoring, testing, and version control. The outcome is a disciplined, controlled approach to AI-augmented development that enhances productivity without compromising code quality or architectural clarity.
By the end of this module, participants will be able to:
- Collaborate effectively with AI coding agents throughout a full development lifecycle
- Translate structured requirements into iterative, AI-assisted implementations
- Guide, refine, and correct AI-generated code to maintain architectural consistency
- Apply AI-assisted refactoring, optimization, and testing practices responsibly
- Manage AI-driven development workflows using Git-based version control
AI-Assisted Development Foundations
Introduction to AI Coding Agents
Requirements-Driven Development
Writing structured requirements in Markdown
- AI Practice: Define business features, tech stack, constraints
AI-Driven Code Generation
Initial code generation from Markdown
- AI Practice: AI scaffolds project structure, modules, sample classes
Git initialization & workflow
- AI Practice: AI scaffolds Git repo, branches, commits
Implementing first features
- AI Practice: AI generates requested features from prompts
Iterative feature addition
- AI Practice: AI generates multiple features based on requirements
Code Evolution and Refactoring
Refactoring & restructuring
- AI Practice: AI suggests modularization, patterns, and code improvements
Optimizing code
- AI Practice: AI identifies inefficiencies, suggests performance improvements
Feature Expansion and AI Collaboration
Adding new functionality post-generation
- AI Practice: AI implements new features on existing codebase
Guiding and correcting AI
- AI Practice: AI applies corrections based on student guidance
Testing and Validation
Testing & code validation
- AI Practice: AI scaffolds unit/integration tests and verifies functionality
Capstone Project
End-to-end workflow
- AI Practice: AI integrates requirements, features, refactoring, tests, and Git history
- Roles: Software Engineers, Full-stack Developers, Backend Developers
- Seniority: Junior to Mid Professionals building structured AI-assisted development skills
• Solid understanding of the language used in the showcases
• Familiarity with object-oriented programming concepts
• Basic knowledge of Git and version control workflows
• Understanding of backend application structure
Sessions can be delivered via the following formats:
- Live Online – Interactive virtual sessions via video conferencing
- On-Site – At your organization’s premises
- In-Person – At Code.Hub’s training center
- Hybrid – A combination of online and in-person sessions
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