AI-assisted full-stack app development and deployment

Why Choose Garranto Academy for Your AI-Assisted Full-Stack Training?

Garranto Academy offers hands-on learning, real-world app-building experience, and expert-led guidance tailored for beginners to mid-level developers. Our program ensures you gain practical, deployable skills to ship AI-assisted applications confidently.

Course Overview:

The AI-Assisted Full-Stack App Development and Deployment course is a 3-day, beginner-friendly, hands-on program that teaches developers how to use AI coding assistants to accelerate full-stack application development. Participants will build a functional CRM/HRMS web application from scratch, covering data modeling, backend API development, frontend UI creation, automated testing, and secure cloud deployment. The program focuses on practical prompt engineering, safe coding practices, and efficient workflow automation. Learners will also explore CI/CD concepts and best practices for AI-assisted development. By the end of the course, participants will deploy their fully working application to a cloud VM with HTTPS and gain the confidence to independently design, build, and ship production-ready AI-assisted applications.

What You’ll Learn in Our AI-Assisted Full-Stack App Development and Deployment Course?

Course Objectives:

Upon successful completion of this course, learners will be able to:

• Understand and apply effective prompt patterns for AI-assisted coding.

• Design and structure core CRM/HRMS entities with clear data relationships.

• Develop and secure a lightweight REST API with authentication features.

• Build a simple web UI that seamlessly integrates with the backend API.

• Write essential tests and leverage AI to expand test coverage.

• Package the application using containers for reliable deployment.

• Set up a cloud VM along with DNS configuration and TLS security.

• Implement a basic CI/CD pipeline to automate deployments to the VM.

Prerequisites

• Basic programming and tool installation skills

• Familiarity with Git and command line

• Basic JavaScript, SQL, and HTTP knowledge

Course Outlines:

Module 1.1 — AI coding assistant for full-stack development: Concepts

• Key Concepts: Prompt shapes for code generation; in-editor completions vs.chat; scaffolding projects and files; using AI for refactors and docs; test suggestion workflows; handling secrets and privacy; reviewing diffs with AI; avoiding hallucinated APIs and packages.

Module 1.2 — AI coding assistant for full-stack development: Hands-On Lab

• Scenario: Kick-off a new repo and let the AI assistant scaffold the baseline stack and tasks.

• Steps:

• Initialize a Git repo and set editor/assistant extensions.

• Draft user stories (e.g., employee record CRUD, login) and ask AI to generate a task list.

• Use AI to scaffold a backend (FastAPI) and frontend (React + Vite) folder structure.

• Prompt AI to add a .env.example, README, and basic scripts.

• Generate initial unit test stubs and run tests.

• Review AI changes via pull request descriptions generated by AI.

• Deliverables: Project repo with scaffolded backend/frontend, initial tests, and README.

Module 2.1 — Build the CRM/HRMS app: Concepts

• Key Concepts: Minimal data model (User, Employee, Department, Role); REST endpoints and pagination; authentication with JWT; input validation and error handling; frontend state and API consumption; accessibility and form

validation; logging and basic metrics.

Module 2.2 — Build the CRM/HRMS app: Hands-On Lab

• Scenario: Implement a working vertical slice: secure login, employee list, and create/edit employee.

• Steps:

• With AI help, define SQLAlchemy models and FastAPI routes for auth and employee CRUD (SQLite for simplicity).

• Add Pydantic schemas, validation, and error responses; test with HTTPie or curl.

• Implement JWT login and route protection; verify 401/403 paths.

• Ask AI to suggest unit tests for models/routes; run and fix failures.

• Scaffold a React UI (login, list, create/edit forms) that calls the API; handle loading and errors.

• Add basic logging and a health endpoint; commit small, reviewed changes.

• Deliverables: Running API + React UI, test report, and short demo video or screenshots.

Module 3.1 — Deploy to cloud VM and domain: Concepts

• Key Concepts: VM provisioning (Ubuntu), firewall and SSH keys; containerizing with Docker; reverse proxy with Nginx; environment variables and secrets; DNS A/AAAA records; Let’s Encrypt TLS and renewals; minimal CI/CD from GitHub to VM.

Module 3.2 — Deploy to cloud VM and domain: Hands-On Lab

• Scenario: Ship the app to a cloud VM and make it reachable at https://your-domain.

• Steps:

• Provision an Ubuntu VM (e.g., AWS Lightsail/EC2), add an SSH key, and lock down the firewall.

• Containerize API and frontend; build images and run locally; push to a registry.

• Install Docker and Nginx on the VM; pull images and run containers with a compose file.

• Configure Nginx as reverse proxy to the containers; obtain TLS certs with Certbot; verify HTTPS.

• Create DNS A record for the domain; confirm external access with health checks.

• Add a lightweight GitHub Actions workflow to build and deploy on tag or main branch.

• Deliverables: Live HTTPS endpoint on your domain, compose file, Nginx config, and CI/CD workflow YAML

Course Outcomes:

Upon completing the "AI-ASSISTED FULL-STACK APP DEVELOPMENT AND DEPLOYMENT" course, participants will:

• Use AI coding assistants to scaffold, refactor, and document full-stack code efficiently.

• Design and build secure REST APIs with proper authentication and validation.

• Develop a functional web interface and integrate it seamlessly with backend services.

• Write and run basic tests while leveraging AI to enhance test quality.

• Containerize applications using Docker for consistent and repeatable deployments.

• Configure DNS settings and SSL/TLS certificates for secure cloud hosting.

• Deploy a working full-stack application to a live cloud VM.

• Automate simple CI/CD workflows to speed up development and deployment cycles.

Key Benefits of AI-Assisted Full-Stack App Development and Deployment

AI-assisted development accelerates coding, reduces errors, and streamlines full-stack workflows from backend to frontend. It enables faster delivery, better code quality, and more efficient deployment with minimal manual effort.

How AI Can Transform Your Full-Stack Development Workflow

AI enhances productivity by generating boilerplate code, improving refactoring, and accelerating debugging. It supports developers across the entire stack, making development faster, smoother, and more scalable.