An AI Requirements Engineering Curriculum for the Vibe Coding Era

How AI is changing the way we work, and a requirements-engineering-based strategy for collaborating with AI

Who This Course Is For

👨‍💻

Developers who want to move beyond toy projects and build production-grade services

🌐

Companies that want to design architecture at global scale

☁️

Teams that want a competitive edge through the latest cloud technologies

📚

Anyone who wants to master complex technology quickly and easily

How This Course Differs

Hands-on, process-driven training that takes you all the way to production-grade deliverables. See what sets this course apart.

Comparison	This Course	Other Courses for General Audiences
Key Points	Spec-First: Proceeds in the order of planning → design → implementation, so AI is applied under a clear blueprint. Software engineering discipline: Prioritizes code quality and structure, pursuing efficiency and completeness from the planning stage	Implementation-First: Tends to jump straight into coding without design documents, coding whatever the AI dictates Speed and convenience: Emphasizes producing many outputs in a short time. A "skip the hard theory, just build fast" approach
Curriculum Structure	End-to-end project: The full flow from requirements definition → design → implementation → testing/deployment is practiced in a single project. Focused on hands-on, real-world processes	Many sample projects: Each chapter practices an individual feature or tool. Built around the latest trending technologies (e.g., ChatGPT, Cursor)
Deliverables, Quality / Completeness	High-quality deliverables: Structurally sound, maintainable code. Aims for production-grade quality (with scalability and stability in mind)	Quick prototype level: The main output is an MVP built in a short time. It works, but code quality and scalability are secondary

Key Points
This Course	Spec-First: Proceeds in the order of planning → design → implementation, so AI is applied under a clear blueprint. Software engineering discipline: Prioritizes code quality and structure, pursuing efficiency and completeness from the planning stage
Other Courses for General Audiences	Implementation-First: Tends to jump straight into coding without design documents, coding whatever the AI dictates Speed and convenience: Emphasizes producing many outputs in a short time. A "skip the hard theory, just build fast" approach
Curriculum Structure
This Course	End-to-end project: The full flow from requirements definition → design → implementation → testing/deployment is practiced in a single project. Focused on hands-on, real-world processes
Other Courses for General Audiences	Many sample projects: Each chapter practices an individual feature or tool. Built around the latest trending technologies (e.g., ChatGPT, Cursor)
Deliverables, Quality / Completeness
This Course	High-quality deliverables: Structurally sound, maintainable code. Aims for production-grade quality (with scalability and stability in mind)
Other Courses for General Audiences	Quick prototype level: The main output is an MVP built in a short time. It works, but code quality and scalability are secondary

Core Curriculum

1. The Software Development Paradigm in the AI Era

Key Learning Content and Activities

1. Analyzing the shift in development paradigms: Analyze how the emergence of AI is changing the software development life cycle (SDLC).

2. Understanding 'vibe coding': Learn the concept, strengths and weaknesses of 'vibe coding' — developing through intuition and conversation without a clear specification.

Learning Objectives

• Understand how AI affects the development process and explain the characteristics of the new development paradigm.
• Recognize the importance of spec-driven development.

🛠️ Tools:

Articles on the latest AI development trends Research materials

2. Hands-on AI Development Tools and Their Limitations

Key Learning Content and Activities

1. UI-centric development lab: Use a UI-centric AI development tool (Lovable) to rapidly visualize ideas and build prototypes.

2. IDE-based development lab: Use a spec-based AI development tool (IDE) to generate code for a small project, and analyze first-hand its usefulness and limitations (such as the difficulty of applying it to large projects).

Learning Objectives

• Understand the characteristics of different types of AI development tools and use them hands-on.
• Clearly recognize the technical constraints of today's AI development tools and derive the need for a systematic methodology to address them.

🛠️ Tools:

Lovable Claude Code

3. Requirements Definition and Specification Principles

Key Learning Content and Activities

1. Learning requirements engineering principles: Learn how to define and structure requirements that AI can clearly understand and process.

2. Requirements writing lab: Practice writing effective requirements documents, including business goals and constraints, with AI collaboration in mind.

Learning Objectives

• Understand the importance of requirements, which determine the success or failure of AI development projects.
• Strengthen the ability to define and specify high-quality requirements.

🛠️ Tools:

ChatGPT Deep Research Kiro

4. DDD-Based System Analysis and Design

Key Learning Content and Activities

1. EventStorming lab: Based on the defined requirements, run EventStorming in MSAEZ to visualize the system's core domains and processes.

2. Generating the architecture and PRD: Automatically generate the microservices architecture, API specifications and a product requirements document (PRD) with detailed features from the analyzed model.

Learning Objectives

• Gain the ability to turn complex business requirements into a systematic design model.
• Learn how to automate analysis, design and documentation using MSAEZ.

🛠️ Tools:

MSAEZ Kiro Task-master

5. System Implementation through AI Pair Programming

Key Learning Content and Activities

1. Setting up a context-based development environment (Context7, Taskmaster): Connect design artifacts (such as the PRD) to the Cursor IDE and systematically break down and manage development tasks.

2. AI pair programming implementation: When developing the backend/frontend, break complex logic into multiple steps and implement the code using an AI-based IDE.

Learning Objectives

• Learn a methodology for collaborating efficiently with AI to implement high-quality code based on design models and specifications.
• Get the most out of the Cursor IDE's advanced features (context management, multi-step prompting and more).

🛠️ Tools:

Figma MCP Cursor IDE Spring Boot Vue.js

6. Intelligent Test Automation and Quality Assurance

Key Learning Content and Activities

1. Unit/integration test automation: Automatically generate and run unit/integration test cases using the Cursor IDE and MSAEZ.

2. E2E test automation: Connect user scenarios (based on the PRD) with Playwright to generate E2E test scripts and verify them directly in the browser.

Learning Objectives

• Build the ability to secure software quality and stability by automating testing across the entire development life cycle.
• Establish traceability across requirements, design, implementation and testing.

🛠️ Tools:

MSAEZ Cursor IDE Playwright JUnit

7. Deployment Automation and CI/CD Pipeline Construction

Key Learning Content and Activities

1. Automatic generation of deployment scripts: Automatically generate the scripts needed for deployment, such as Dockerfiles and Kubernetes YAML, from the design model in MSAEZ.

2. One-click deployment and pipeline management: Build a CI/CD pipeline using the generated scripts and deploy the application to the cloud with a single click.

Learning Objectives

• Experience a DevOps automation process that deploys finished applications quickly and reliably.
• Develop the ability to build and operate a model-based CI/CD pipeline.

🛠️ Tools:

MSAEZ Git Kubernetes ChatGPT

* Some course content may change depending on circumstances.

Instructors

Jinyoung Jang

uEngine CEO & President

Current CEO of uEngine Solutions
MSA training and enterprise consulting
Runs the MSA Facebook group
Technical advisory member, Digital Platform Government
Certified instructor, Korea Software Technology Association (KOSTA)
SAFe Agile Certified Consultant (SPC)
Numerous cloud (MSA, DDD) lectures
University lecturer in object-oriented programming

Yongju Park

uEngine Director & MSA Consultant

Current lead instructor, uEngine MSA regular curriculum
Current instructor, MSA App. Engineering corporate course
Current adjunct professor, Department of Computer/AI Engineering, Sejong Cyber University
Certified instructor, Korea Software Technology Association (KOSTA)
MSA DT master plan project
Author of microservices course materials and textbooks
Instructor, KT Microservice job transition program
Instructor, LG CNS EventStorming training

Seongyeol Yoon

uEngine Manager & MSA Consultant

Current Head of R&D / Executive Director, NAVIWORKS
Current Head of R&D / Director, DreamFlow
Current certified instructor, KOSTA BAPF Forum
MSA development and network infrastructure construction
Domain analysis/design and server development projects
Vice Chair, TTA IoT Special Technical Committee
Adjunct professor, Gachon University
Delivered numerous microservices training courses

Course Information

Schedule

1-day course
9:30 – 17:30 (7 hours total)

Format

Highly focused live online lectures
Real-time online live lectures (via Zoom)

Tuition

Top-tier training at a reasonable price
KRW 210,000→ KRW 140,000

Contact

help@uengine.org

Service delivery period: the service is delivered within 1 month of payment completion. Payment can be made starting 2 weeks before the course start date.

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