Intelligent quiz application that generates personalized questions from study materials using RAG (Retrieval Augmented Generation), providing real-time feedback and source references to enhance learning efficiency.
Students struggle to effectively test their knowledge from study materials. Traditional methods require manual question creation, lack context-aware feedback, and don't provide direct references to source material when answers are incorrect. This leads to inefficient study sessions and difficulty identifying knowledge gaps.
Built an intelligent RAG-powered quiz system that automatically ingests study documents, generates contextual multiple-choice questions using AI, and provides instant feedback with direct page references. The system uses semantic search to retrieve relevant document chunks, filters questions by specific books/documents, and tracks topic mastery with visual indicators. Students can upload their own materials, get personalized quizzes, and receive AI explanations pointing to exact source locations for incorrect answers.
A full-stack AI-powered quiz platform that transforms study materials into interactive learning experiences. The application uses RAG architecture to ingest PDF and text documents, generate context-aware quiz questions using LangChain and xAI Grok, and provides detailed feedback with page-specific source references. Built with FastAPI backend serving a modern HTML/CSS/JS frontend, the system leverages Qdrant vector database for semantic search and HuggingFace embeddings for local, cost-effective document processing. Features include document upload management, book-specific quiz generation, real-time topic tracking, and intelligent answer validation with AI-generated explanations.
Successfully deployed production-ready AI quiz application. Reduced quiz generation time from manual creation (hours) to automated AI generation (seconds). Students can now upload study materials, receive instant personalized quizzes, and get contextual feedback with direct source references. Deployed on Railway with Qdrant Cloud integration for persistent vector storage.
Full-stack intelligent document Q&A system using RAG (Retrieval-Augmented Generation) that enables users to query PDF and text documents with AI-powered responses, featuring automatic document indexing, vector search, and source citation.
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