JavaScript, TypeScript, React, Next.js, Node.js, APIs, PostgreSQL, authentication, Git, testing, security and deployment.
AI5 ACADEMY PROFESSIONAL CERTIFICATE · A TGC INDIA INITIATIVE · UPDATED OCTOBER 2026
AI-Driven Full Stack Development Course
20-WEEK PROFESSIONAL PROGRAM · FULL-STACK DEVELOPMENT WITH AI FROM WEEK 1
Learn full-stack development the way modern software teams build—with AI throughout the development lifecycle. Build, inspect, test, debug and deploy complete applications while retaining control of the code.
Learn Full-Stack Development the Way Modern Software Teams Build — With AI Throughout the Development Lifecycle. The fundamentals remain. The way developers build software has changed.
20 WeeksLive Online + ClassroomBeginner FriendlyAI Used From Week 16 Full-Stack Projects
FULL-STACK DEVELOPMENT FOR THE AI-DRIVEN SOFTWARE ERA
Learn enough engineering to control what AI builds.
Approximately 70–80% of the learning outcome is full-stack software development. AI is used throughout the program to plan, build, inspect, modify, debug, test, document and deploy.
Understand → Specify → Plan → Build with AI → Inspect → Modify → Debug → Test → Integrate → Deploy
Specifications, coding agents, code inspection, multi-file changes, AI-assisted debugging, tests, review and documentation from Week 1.
AI can write code. The developer remains responsible for whether that code works safely.
CLEAR COURSE BOUNDARY
What this program is—and is not
It is Full-Stack Software Development taught through an AI-driven development workflow. It is not primarily Data Science, Machine Learning, AI/ML engineering, prompt engineering, LLM research, model training, an Agentic AI or RAG specialisation, a conventional MERN course, or a short vibe-coding workshop.
LLM APIs, structured outputs, tool calling, document interaction, basic RAG and basic agent integration appear only as modern application-development capabilities. Learners can continue into AI5 specialist programs for deeper study.
EMPLOYER OUTCOME
What a graduate should be able to do
Read and modify an existing codebase; build frontend interfaces, backend systems and database-backed features; build and consume APIs; use Git and GitHub; integrate external services; direct coding agents; inspect incorrect output; debug, test and refactor applications; apply basic application-security checks; deploy software; and maintain existing applications.
The graduate is trained as a developer who works with AI—not as someone who only prompts AI.
22+years in training
20,000+learners across TGC
5classroom locations
Live onlinejoin from anywhere
NEXT START OPTIONS
Choose a live batch.
COURSE OVERVIEW
What this course
is built to do.
Become a full-stack developer for the AI-driven software era. Learn frontend, backend, databases, APIs, testing, security and deployment while using coding assistants and agents from Week 1. You remain responsible for understanding and checking every material change.
Each module combines a live trainer demonstration, guided lab, assignment and review before the next stage.
CONNECTED CAREER DIRECTIONS
Full-Stack DeveloperSoftware DeveloperWeb Application DeveloperFull-Stack EngineerJunior Software EngineerAI-Assisted Full-Stack Developer
IS THIS COURSE RIGHT FOR YOU?
Choose it for the right reason.
You want to become a full-stack developer using AI from Week 1
You want to understand and control generated code—not merely prompt for it
You want a portfolio covering frontend, backend, databases, APIs, testing and deployment
YOUR LEARNING ARC
From guided foundation to finished work.
Understand & specify
Turn a product idea into requirements, user stories and acceptance checks.
Plan & build with AI
Break work into safe tasks and direct coding assistants across the codebase.
Inspect & modify
Read generated code, correct architecture and change features without losing control.
Debug, test & integrate
Trace failures and verify interfaces, APIs, data, permissions and edge cases.
Deploy & maintain
Ship documented full-stack applications and handle production changes.
INDUSTRY TASKS
Practise the work, not only the tool.
- Read and modify an existing codebase
- Build responsive frontend interfaces
- Create backend logic, APIs, authentication and user roles
- Design and query a relational database
- Test, secure and deploy a complete application
PRO
LED
LED
YOUR TRAINING TEAM
Learn from experienced working professionals.
Live demonstrations, guided practice and direct project feedback are part of the course. Trainers update examples and tool coverage as professional practice changes.
TRAIN A PROFESSIONAL TEAM
Run this programme around your tools and operating controls.
Private weekday, weekend and fast-track formats are available for organisations.
WHAT YOU WILL BE ABLE TO DO
Course outcomes
01
Build and maintain full-stack React and Next.js applications
Ship frontend, backend, APIs, database, authentication and permission flows.
02
Direct and verify AI coding agents
Plan repository tasks, inspect generated code, modify it and review the result.
03
Add useful AI features to web applications
Integrate an LLM API, structured output, document interaction or a bounded tool call.
04
Test, debug and secure generated software
Use unit, integration and browser tests plus checks for common application risks.
05
Deploy and maintain production applications
Manage environments, production data, logs, documentation and release checks.
06
Present a portfolio-ready full-stack capstone
Take one product from specification and architecture through production deployment.
TAKE THE NEXT STEP
Need fees, syllabus or the right batch?
An AI5 Academy advisor can help you compare mode, schedule and starting level.
DETAILED COURSE FLOW
10 learning modules
Understand → Specify → Plan → Build with AI → Inspect → Modify → Debug → Test → Integrate → Deploy. AI is used throughout; the developer remains responsible for the software.
Weeks 1–2 · Modern Developer Foundations
Understand how web applications work and begin using AI as a checked development partner from the first week.
CORE TOPICS
- Frontend, backend, database and API concepts
- Development environment and terminal basics
- Git and GitHub workflow
- JavaScript and TypeScript foundations
- Reading code, tracing execution and understanding errors
- Safe AI use, specifications and acceptance checks
CURRENT WORKING TOOL STACK
VS Code · Git · GitHub · ChatGPT · Cursor · OpenAI Codex
GUIDED PRACTICE
Use an assistant to explain and improve a small codebase, then verify each change manually.
MODULE DELIVERABLE
Project 1 · Responsive product web application
Weeks 3–5 · AI-Assisted Frontend Development
Build responsive interfaces while learning the frontend concepts needed to judge and change generated code.
CORE TOPICS
- HTML and CSS foundations
- JavaScript and TypeScript
- React and Next.js
- Components, state, forms and routing
- Responsive UI and accessibility
- Frontend architecture, performance and testing
CURRENT WORKING TOOL STACK
React · Next.js · TypeScript · AI coding assistants
GUIDED PRACTICE
Turn a UI specification into components, inspect the output, correct defects and test responsive behaviour.
MODULE DELIVERABLE
Frontend system for Projects 1 and 2
Weeks 6–7 · AI-Assisted Backend Development
Build server-side features and understand how requests, business rules, identity and errors are handled.
CORE TOPICS
- Node.js and server-side architecture
- REST APIs and request handling
- Validation and error handling
- Authentication and authorisation
- External services and environment variables
- Backend security foundations
CURRENT WORKING TOOL STACK
Node.js · Next.js server features · REST APIs · GitHub
GUIDED PRACTICE
Direct AI to implement one backend feature, then trace requests and test success, failure and permission paths.
MODULE DELIVERABLE
Backend and authentication for Project 2
Week 8 · Databases & Application Data
Design reliable application data and check every generated schema, query and migration.
CORE TOPICS
- Relational database concepts
- PostgreSQL schemas and relationships
- CRUD and queries
- Migrations and data validation
- Database access and security
- AI-assisted query debugging and optimisation
CURRENT WORKING TOOL STACK
PostgreSQL · Supabase · SQL
GUIDED PRACTICE
Create a data model with AI support, then check relationships, constraints, queries and access rules.
MODULE DELIVERABLE
Database layer for Project 2
Weeks 9–10 · Full-Stack Integration
Connect the application layers through a specification-led workflow.
CORE TOPICS
- Requirements and user stories
- Architecture and development plans
- Frontend, backend and database integration
- API contracts and third-party services
- Authentication across the stack
- Integration testing and failure handling
CURRENT WORKING TOOL STACK
React · Node.js · PostgreSQL · REST APIs
GUIDED PRACTICE
Take an idea through requirements, architecture, implementation and checked integration.
MODULE DELIVERABLE
Project 3 · Third-party API application
Weeks 11–12 · Professional AI Coding Workflows
Use coding assistants and agents for codebase work that goes beyond one-off code generation.
CORE TOPICS
- Cursor, Claude Code, Codex and Copilot workflows
- Context management and project instructions
- Codebase navigation and multi-file changes
- Refactoring and AI-assisted debugging
- AI-assisted tests, review and documentation
- Git branches, commits and pull-request habits
CURRENT WORKING TOOL STACK
Cursor · Claude Code · OpenAI Codex · GitHub Copilot
GUIDED PRACTICE
Plan and complete a multi-file change with tests, a review record and a clear rollback point.
MODULE DELIVERABLE
Reviewed codebase change
Weeks 13–14 · AI Capabilities in Full-Stack Applications
Add useful AI features without turning the program into an LLM, RAG or agent specialisation.
CORE TOPICS
- LLM APIs and streaming
- Structured outputs
- Tool and function calling
- Document interaction
- Basic RAG integration
- Basic agent and MCP integration awareness
CURRENT WORKING TOOL STACK
LLM APIs · Structured outputs · Tool calling · Basic RAG
GUIDED PRACTICE
Add one bounded AI feature with error handling, cost limits and human checks to a full-stack application.
MODULE DELIVERABLE
Project 4 · Full-stack application with a useful AI feature
Weeks 15–16 · Testing, Security & Production Readiness
Treat generated code as unverified until behaviour, risks and failure paths have been checked.
CORE TOPICS
- Unit, integration and browser testing
- AI-assisted debugging and code review
- Incorrect packages and broken assumptions
- Authentication, secrets and dependency risks
- Input validation, SQL injection and XSS
- Performance, logging and production debugging
CURRENT WORKING TOOL STACK
Playwright · GitHub · AI code reviewers
GUIDED PRACTICE
Use AI to draft tests, add missing edge cases, run a security review and correct the application.
MODULE DELIVERABLE
Quality and security record for Project 5
Weeks 17–18 · Deployment & Maintenance
Ship a full-stack application and learn how to operate it after release.
CORE TOPICS
- Production hosting and domains
- Environment variables and secrets
- Production databases
- CI/CD foundations
- Monitoring and logs
- AI-assisted deployment diagnosis and maintenance
CURRENT WORKING TOOL STACK
Vercel · Supabase · GitHub Actions · Docker concepts
GUIDED PRACTICE
Deploy an application, inspect logs and fix a staged production fault.
MODULE DELIVERABLE
Project 5 · Deployed AI-enabled SaaS application
Weeks 19–20 · Portfolio & Capstone
Prove the ability to build software, not merely generate code.
CORE TOPICS
- Idea and user problem
- Specification and architecture
- AI-assisted implementation
- Testing, debugging and security review
- Deployment and documentation
- Code walkthrough and portfolio presentation
CURRENT WORKING TOOL STACK
Full program stack
GUIDED PRACTICE
Defend architecture and code decisions, demonstrate failure handling and explain every material AI-assisted change.
MODULE DELIVERABLE
Project 6 · Production full-stack capstone
HOW THE TRAINING WORKS
Learn it. Apply it. Get it reviewed. Improve it.
Every important skill moves through explanation, demonstration, guided use and independent application. Trainer feedback is used to revise the work before it becomes part of the final portfolio.
Concept briefing
The trainer explains the principle, use case, limitations and the quality standard expected.
Live demonstration
A complete task is demonstrated while the trainer explains decisions, checks and common mistakes.
Guided lab
Learners repeat the method with support, ask questions and correct problems during the session.
Applied assignment
The same method is used on a different brief so the learner must make independent decisions.
Review and revision
Work is checked against a rubric, revised after feedback and prepared for project presentation.
PROGRESS IS CHECKED THROUGHClass exercisesModule deliverablesProject reviewsFinal capstone presentation
PORTFOLIO WORK
Projects you can show
Responsive Product Web Application
Plan, produce, test and present a finished piece with trainer feedback.
Authenticated Full-Stack Application
Plan, produce, test and present a finished piece with trainer feedback.
Third-Party API Application
Plan, produce, test and present a finished piece with trainer feedback.
Full-Stack Application with a Useful AI Feature
Plan, produce, test and present a finished piece with trainer feedback.
AI-Enabled SaaS Application
Plan, produce, test and present a finished piece with trainer feedback.
Production Full-Stack Capstone
Plan, produce, test and present a finished piece with trainer feedback.
TAKE THE NEXT STEP
Need fees, syllabus or the right batch?
An AI5 Academy advisor can help you compare mode, schedule and starting level.
FEES & NEXT BATCH
Request current fee and batch details
Admissions will share the current fee, payment options, confirmed schedule and learning mode for the next suitable cohort.
TOOLS COVERED
AI coding workflow
CursorClaude CodeOpenAI CodexGitHub CopilotChatGPT
Development environment
VS CodeGitGitHubTerminal
Full-stack engineering
HTMLCSSJavaScriptTypeScriptReactNext.jsTailwind CSSNode.jsPostgreSQLSupabaseREST APIsAuthentication
AI features inside applications
LLM APIsStructured outputsTool callingDocument interactionBasic RAG integrationBasic agent integrationMCP awareness
Testing & deployment
GitGitHubPlaywrightDocker fundamentalsGitHub ActionsVercel
The working tool set may change as coding tools change. The program teaches specifications, codebase context, inspection, testing and review methods that transfer between tools.HOW THE APPROACHES DIFFER
From tool assistance to an end-to-end development method
| Dimension | Traditional Full Stack | AI-Assisted Coding | AI-Driven Full Stack |
|---|---|---|---|
| Coding fundamentals | Core focus | Required | Required and checked |
| Frontend, backend, databases & APIs | Full coverage | Varies | Full coverage |
| AI coding tools | Optional | Used for selected tasks | Used across the lifecycle |
| Coding agents & multi-file work | Usually absent | Occasional | Planned and reviewed |
| Specification-driven development | Team dependent | Sometimes | Core workflow |
| AI debugging, testing & review | Limited | Task based | Built into every project |
| Code inspection | Developer led | Can be inconsistent | Mandatory before acceptance |
| AI features in applications | Separate topic | Optional | Bounded integration |
| Deployment & production readiness | Covered | Varies | Covered with AI-assisted diagnosis |
CONTINUE YOUR AI5 PATH
Full-stack career program first; specialist depth when you need it
This 20-week program is the complete software-building route. Choose the four-week coding-assistant lab for a shorter workflow upgrade. Continue to Generative AI or Agentic AI when you want deeper AI-system study.
COMMON QUESTIONS
Before you apply
Do I need coding experience?
No professional coding experience is required. Serious beginners learn programming logic, web foundations, terminal and Git during the opening stage, then practise throughout the program.
＋Is this a no-code or vibe-coding course?
No. Students learn frontend, backend, databases, APIs, testing, Git, security and deployment, and must inspect and verify AI-generated code.
＋Is this an AI or Machine Learning course?
No. The main outcome is full-stack software development. It does not teach model training, data science or Machine Learning as its main subject.
＋Which AI coding tools will I learn?
Core labs use a sensible working set such as Cursor, Claude Code, OpenAI Codex, GitHub Copilot and ChatGPT. Methods are taught so learners can move between tools.
＋Will I learn programming fundamentals?
Yes. JavaScript, TypeScript, React, Next.js, Node.js, APIs, PostgreSQL, Git, testing and deployment remain part of the program.
＋Will I add AI features to applications?
Yes, at an application-integration level: LLM APIs, structured outputs, tool calling, document interaction and basic RAG or agent integration. Deeper study belongs in AI5 specialist programs.
＋What projects will I build?
You will build six portfolio projects: responsive frontend, authenticated full stack, third-party API, useful AI feature, SaaS-style application and a production capstone.
＋Will I learn prompting?
You will write specifications and instructions for coding tools, but this is not a prompt-engineering course. The main work is building and checking software.
＋What can an employer expect?
A graduate should be able to read a codebase, build and connect application layers, use Git, direct coding agents, inspect output, debug, test, apply basic security checks and deploy.
＋Will the tools become outdated?
Individual tools will change. AI5 can update labs while retaining the software-development method, code review habits and verification standards.
＋Are classes live or recorded?
Classes are trainer-led in the classroom or live online.Recordings may support revision but do not replace class.
＋Will I receive a certificate?
Yes. Course completion requires attendance, assignments and the final project.
＋Can working professionals join?
Yes. Weekday, weekend and selected fast-track schedules are available.
＋NEXT BATCH
Choose your course.
Choose your schedule.
Online or offline. Weekdays or weekends. Regular or fast track. Speak with an AI5 Academy advisor about the right starting level.
COURSE PLANNING
AI-Driven Full Stack Development Course: syllabus, projects and admission details
Learn full-stack development the way modern software teams build—with AI throughout the development lifecycle. Build, inspect, test, debug and deploy complete applications while retaining control of the code.
AI-Driven Full Stack Development Course syllabus
AI foundations, Prompt systems, Practical workflows. Read the full curriculum above for the complete learning sequence.
AI-Driven Full Stack Development Course projects
Responsive Product Web Application; Authenticated Full-Stack Application; Third-Party API Application. Check the project section for the work expected in this program.
AI-Driven Full Stack Development Course eligibility
Entry level: Beginner Friendly. Ask a counsellor to confirm whether your background meets the program prerequisites.
AI-Driven Full Stack Development Course duration
Program duration: 20 weeks. Confirm the next batch timetable before joining.
AI-Driven Full Stack Development Course tools
OpenAI Codex, Claude Code, Cursor, GitHub Copilot, VS Code, GitHub. Tool access and any paid accounts should be confirmed for your cohort.
AI-Driven Full Stack Development Course fees and batches
Training mode: Online + Offline. Request current fees, available batches and a suitable schedule.
Get course details →