Professional Vibe Coding: Architecture to Delivery

Course Description

Master the art of professional software engineering with AI assistance. In this comprehensive course, technical professionals learn to leverage AI tools to architect systems, design solutions, manage development workflows, and deliver production-ready applications. Through emphasis on engineering best practices and real-world project scenarios, you'll learn how to use AI as a force multiplier while maintaining architectural integrity, code quality, and professional development standards. This course bridges the gap between "prompting AI to write code" and building enterprise-grade software systems with AI assistance.

Prerequisites:

-  Experience with at least one programming language and basic software development concepts

-  Understanding of version control (Git) and basic CI/CD concepts

-   Familiarity with web applications or cloud services helpful but not required

-  No prior AI coding experience required—we'll start from professional foundations

Course Outline

Module 1: AI-Assisted Architecture Design

Explore architectural patterns and system design with AI collaboration
Conduct requirement analysis conversations that extract complete context
Generate system context diagrams, component architectures, and data models
Create Architecture Decision Records (ADRs) documenting design choices
Build technology stack decision matrices with trade-off analysis

Module 2: Technical Design Documentation

Develop comprehensive technical design documents using AI assistance
Structure subsystem documentation with clear responsibilities and contracts
Generate API specifications, data models, and interface definitions
Create deployment architecture and infrastructure diagrams
Maintain living documentation that evolves with the codebase

Module 3: Design Review Process & Validation

Prepare architecture presentations with AI-generated supporting materials
Use AI as a first-pass reviewer to identify risks and bottlenecks
Generate comparison documents showing alternatives considered
Anticipate and prepare responses for technical review questions
Document design decisions and action items from review sessions

Module 4: Backlog Creation & Epic Breakdown

Transform architecture into implementable epics using structured AI prompts
Generate user stories with acceptance criteria and technical notes
Decompose technical tasks into manageable 2-4 hour work blocks
Identify dependencies, risks, and integration points across stories
Estimate complexity using AI-assisted analysis of hidden requirements

Module 5: Sprint Planning with AI

Structure sprint planning sessions using AI for estimation and clarification
Generate acceptance test templates before implementation begins
Create initial implementation approach documents for complex features
Build sprint backlogs with clear task hierarchies and dependencies
Establish definition of done criteria for each work item

Module 6: Professional Development Workflow

Set up development environments with Docker, configs, and tooling
Implement test-first development using AI to generate test suites
Follow the implementation cycle: context → tests → code → refactor → document
Maintain project context files for consistent AI interactions
Practice version control discipline with meaningful commits and branches
Use GitHub Copilot to scaffold test frameworks and boilerplate configurations

Module 7: Code Quality & Review Standards

Review AI-generated code for architecture alignment and security issues
Refactor with AI assistance to improve maintainability and performance
Prepare pull requests with comprehensive descriptions and context
Use AI to perform pre-review analysis of code changes
Document technical debt and create remediation plans

Module 8: Browser Automation & E2E Testing with Puppeteer

Generate Puppeteer test suites using GitHub Copilot for comprehensive browser testing
Create page object models and reusable test helpers with AI assistance
Build user journey tests that simulate real user interactions and workflows
Implement visual regression testing and screenshot comparison strategies
Debug Puppeteer tests with AI-suggested selectors and wait conditions
Generate integration tests that validate subsystem interactions
Create end-to-end test scenarios for critical user workflows
Build test data factories and fixtures using AI assistance
Implement contract testing for API boundaries
Automate testing pipelines with AI-generated CI/CD configurations
Set up headless browser testing in CI/CD with Docker and GitHub Actions

Module 9: Deployment & Operations Planning

Design CI/CD pipelines with deployment strategies and rollback procedures
Generate infrastructure-as-code for consistent environment provisioning
Create monitoring dashboards and alerting configurations
Document runbooks and incident response procedures
Plan capacity and scaling strategies with AI-assisted analysis
Configure automated smoke tests using Puppeteer for post-deployment validation

Module 10: Team Collaboration & Knowledge Transfer

Establish team conventions and AI prompting standards
Create onboarding documentation for new team members
Conduct effective code reviews in AI-assisted development contexts
Run retrospectives analyzing AI effectiveness and process improvements
Build shared prompt libraries and architectural patterns
Share Puppeteer test patterns and debugging techniques across the team

Hands-On Experience

Expect 60% of the course to be project-based, featuring a full software development lifecycle for a multi-tier application. Students will architect, design, plan, implement, test, and deploy a complete system using professional vibe coding practices. The capstone project includes comprehensive Puppeteer test suites covering user authentication, data workflows, and cross-browser compatibility. Students will experience architecture presentations, sprint execution, code reviews, automated testing pipelines, and production deployment planning.

Featured Hands-On Labs:

Lab 3.1: Generate Puppeteer test scaffolding with GitHub Copilot
Lab 3.2: Build page object models for authentication flows using AI prompts
Lab 3.3: Create data-driven tests with dynamic test data generation
Lab 3.4: Implement visual regression testing with screenshot baselines
Lab 3.5: Debug flaky tests using Copilot-suggested wait strategies and selectors
Lab 3.6: Integrate Puppeteer tests into CI/CD with parallelization

Skills You'll Gain

After completing Professional Vibe Coding, you'll be able to:

-       Architect scalable systems through structured conversations with AI assistants

-       Create comprehensive technical design documents and architecture diagrams

-       Lead design reviews with well-prepared materials and decision documentation

-       Break down complex projects into implementable epics, stories, and tasks

-       Execute professional development workflows with test-first AI-assisted coding

-       Generate and maintain browser automation tests using Puppeteer and GitHub Copilot

-       Debug complex UI interactions and create reliable E2E test suites with AI assistance

-       Implement visual regression testing and cross-browser compatibility validation

-       Maintain high code quality standards while leveraging AI code generation

-       Manage development backlogs and sprint planning with AI tooling

-       Deploy and operate systems with AI-generated infrastructure and monitoring

-       Establish vibe coding best practices and standards for development teams

-       Evaluate AI-generated solutions critically and make informed architectural decisions