AI-Augmented Development with Coding Agents
Description
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.
Indicative Duration: 24 training hours
*Duration is adjusted based on the final scope and the target audience.
Scope
| 1. AI-Assisted Development Foundations | • Introduction to AI Coding Agents |
|
| 2. Requirements-Driven Development | • Writing structured requirements in Markdown • AI Practice: Define business features, tech stack, constraints |
|
| 3. AI-Driven Code Generation | • Initial code generation from Markdown • AI Practice: AI scaffolds project structure, modules, sample classes |
|
|
4. AI-Assisted Development Workflow
|
• 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 |
||
|
5. Code Evolution & Refactoring
|
• Refactoring & restructuring • AI Practice: AI suggests modularization, patterns, and code improvements |
|
| • Optimizing code • AI Practice: AI identifies inefficiencies, suggests performance improvements |
||
|
6. Feature Expansion & 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 |
||
| 7. Testing & Validation | • Testing & code validation • AI Practice: AI scaffolds unit/integration tests and verifies functionality |
|
| 8. Capstone Project | • End-to-end workflow • AI Practice: AI integrates requirements, features, refactoring, tests, and Git history |
|
Learning Objectives
Upon completion of the course 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
Target Audience
- Roles: Software Engineers, Full-stack Developers, Backend Developers
- Seniority: Junior to Senior Professionals building structured AI-assisted development skills
Prerequisite Knowledge
- Solid understanding of at least one language (e.g., Java, Python, C#, JavaScript) to be used in the showcased examples
- Familiarity with object-oriented programming concepts
- Basic knowledge of Git and version control workflows
- Understanding of backend application structure
Delivery Method
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
