Build Production-Ready AI-Powered Full-Stack Web Applications with React & LLMs

About Course

Master building modern, production-grade AI applications using React 19, TypeScript, Bun/Node.js, and Large Language Models (OpenAI, Gemini, Ollama). This hands-on course includes two major projects: a professional Chatbot and an AI Review Summarizer SaaS app. Ideal for intermediate developers in Bangladesh who want to ship real AI features. Duration: 36 live/recorded classes (72 hours total) | Tech Stack: React + Vite + shadcn/ui + Tailwind, Hono/Bun backend, Prisma, Ollama local models | Prerequisites: Strong JS, basic React & Git.

Build full-stack AI applications from scratch using React 19, TypeScript, Bun/Node.js, and LLMs
Integrate OpenAI, Google Gemini, and local open-source models (Ollama) securely and efficiently
Master professional prompt engineering to get reliable, high-quality AI outputs
Create modern, responsive user interfaces with shadcn/ui, Tailwind CSS, and TanStack Query
Design clean, maintainable backend architecture with Hono, Zod validation, and Prisma ORM
Handle real-world challenges: streaming responses, conversation memory, error handling, rate limiting
Deploy production-grade AI apps to Vercel with custom domains and zero-downtime
Switch between cloud APIs and free local models to eliminate ongoing costs
Build a strong portfolio project ready for job applications or freelance clients
Follow industry best practices for code quality, security, testing, and accessibility

Course Content

Topic 1: Course Foundation & Modern Development Environment
(Classes 1–3 | ~6 hours) Lesson 1.1: Welcome, Course Roadmap & Professional Mindset (Theory + Motivation) Lesson 1.2: Prerequisites Refresh – Modern JS/TS Features & Tooling Mastery Lesson 1.3: First Full-Stack “Hello AI” App – Setup & Initial Connection Quiz 1.1: Basic Setup & Tools Quiz (5–8 MCQs: Git, Bun install, env vars, etc.)

Topic 2: Large Language Models Deep Dive
(Classes 4–7 | ~8 hours) Lesson 2.1: What LLMs Are + Tokens, Context Window & How They Work Lesson 2.2: Model Selection – GPT-4o, Gemini, Grok, Llama Comparison Lesson 2.3: Making Your First API Calls – Streaming, Parameters & Prompts Lesson 2.4: Cost, Rate Limits, Safety & Best Practices Quiz 2.1: LLM Fundamentals Quiz (10 questions: tokens, temperature, model pros/cons)

Topic 3:Professional Full-Stack Architecture Setup
(Classes 8–12 | ~10 hours) Lesson 3.1: Project Structure – Monorepo with Bun Workspaces & TypeScript Lesson 3.2: Backend Foundations – Hono + Zod Validation + Error Handling Lesson 3.3: Frontend Foundations – React 19 + Vite + shadcn/ui + Tailwind Lesson 3.4: Connecting Frontend & Backend – Proxy, CORS, .env Security Lesson 3.5: Tooling & Quality – Prettier, ESLint, Husky + First End-to-End Test Quiz 3.1: Full-Stack Setup Quiz (validation, architecture, tooling)

Topic 4: Project 1 – Production-Grade AI Chatbot
(Classes 13–22 | ~20 hours – Core hands-on project) Lesson 4.1: Building the Chat API – Secure LLM Call & Conversation History Lesson 4.2: Streaming Responses & State Management with TanStack Query Lesson 4.3: Input Validation, Rate Limiting & Robust Error Handling Lesson 4.4: Clean Architecture – Services, Controllers, Repositories Lesson 4.5: Modern Chat UI – Message Bubbles, Markdown Rendering & Copy Lesson 4.6: UX Enhancements – Typing Indicator, Auto-Scroll, Animations Lesson 4.7: Mobile Responsive Design & Accessibility Basics Lesson 4.8: Edge Cases, Retry Logic & Loading States Lesson 4.9: Testing & Debugging Workshop Lesson 4.10: Deployment to Vercel – Custom Domain & Final Polish Quiz 4.1: Chatbot Project Quiz (practical scenarios + code understanding)

Topic 5: Advanced Prompt Engineering Mastery
(Classes 23–26 | ~8 hours) Lesson 5.1: Anatomy of Perfect Prompts – Context, Role & Structure Lesson 5.2: Few-Shot Examples, Chain-of-Thought & Output Formatting (JSON) Lesson 5.3: Reducing Hallucinations – Self-Critique & Advanced Techniques Lesson 5.4: Live Optimization Exercises – Improve Chatbot Responses Quiz 5.1: Prompt Engineering Quiz (analyze & rewrite bad/good prompts)

Topic 6: Project 2 – AI Review Summarizer SaaS Application
(Classes 27–33 | ~14 hours – Second major project) Lesson 6.1: Database Setup – Prisma + PostgreSQL & Schema Design Lesson 6.2: CRUD API for Reviews + Realistic Data Seeding Lesson 6.3: LLM-Powered Summarization Endpoint & Smart Prompts Lesson 6.4: Frontend Review List – Star Ratings & Infinite Scroll Lesson 6.5: Summary Display, Regenerate & Loading Skeletons Lesson 6.6: Mutations, Optimistic Updates & Error Handling Lesson 6.7: Full Refactor, Accessibility & Final Deployment Quiz 6.1: Summarizer Project Quiz (database, API, UI scenarios)

Topic 7: Open-Source & Local LLMs with Ollama
(Classes 34–35 | ~4 hours) Lesson 7.1: Installing & Running Ollama – Llama 3, Gemma Locally Lesson 7.2: Switching Projects to Ollama API – Cost-Free Production Quiz 7.1: Local vs Cloud Models Quiz (comparison, setup)

Topic 8: Capstone, Deployment & Career Launch
(Class 36 | ~2 hours) Lesson 8.1: Full Course Recap & Best Practices Checklist Lesson 8.2: Portfolio Building, Resume Tips & Next Steps (RAG, Agents Intro) Lesson 8.3: Deployment Mastery & Final Project Polish Final Quiz / Assignment: Build Your Own Mini AI App (submit GitHub link) Certificate (if enabled): Auto-issue on 80%+ completion

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