LLM-Powered Q&A System

Assessment Objective

This project is part of a pre-screening technical assessment designed to demonstrate proficiency in:

• Working with modern full-stack technologies
• Implementing AI/LLM integrations
• Creating user-friendly interfaces
• Writing clean, maintainable code
• Handling API integrations effectively
• Showcasing strong decision-making skills

---

Technical Stack

• Backend: Python (FastAPI)
• Frontend: Next.js (latest version)
• Styling: TailwindCSS
• LLM Integration: ChatGPT

---

Project Overview

LLM-Powered Q&A System is a smart, real-time question-and-answer platform built using FastAPI and Next.js.
It allows users to ask questions and receive detailed, structured, and well-formatted answers powered by a Large Language Model (LLM).

Example use case:

User Input: "What documents do I need to travel from Kenya to Ireland?"
System Response:

• Required visa documentation
• Passport requirements
• Additional necessary documents
• Relevant travel advisories

---

Features

• Interactive chat interface
• Real-time LLM responses
• Well-formatted and structured outputs
• Mobile-responsive UI
• Error handling and graceful fallbacks
• Clean, maintainable codebase

---

How to Run Locally

Backend (FastAPI)

# Navigate to the backend directory
cd backend

# Install dependencies
pip install -r requirements.txt

# Start FastAPI server
uvicorn server:app --reload

Frontend (Next.js)

# Navigate to the frontend directory
cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

---

🔗 Live Demo

llm-powered-q-a-system.onrender.com/

---

✍️ Final Note

I HAVE CLEARLY UNDERSTOOD THE KEY POINTERS FOR THE TECHNICAL ASSESSMENT.