Project Info

Working Title: LLM-Powered PDF RAG System

GitHub Repo Name: RAG-Based-PDF-QA-System

Problem Statement:

Large collections of private documents such as research papers, reports, and manuals are difficult to search using traditional keyword-based methods. Users often waste time locating relevant information across multiple PDF files, and existing tools do not provide precise, context-aware answers to natural language queries.

This project aims to design and implement an LLM-powered Retrieval-Augmented Generation (RAG) system that enables users to upload private PDF documents and query them using natural language by combining semantic retrieval with generative models.

Objectives

• Enable natural-language querying over private PDF document collections
• Retrieve relevant document passages using vector similarity search
• Generate grounded answers using an LLM
• Reduce hallucinations via retrieval and citation
• Provide a simple web interface for interaction

Core Concepts

What is RAG?

A system that retrieves relevant document chunks from an external knowledge store and supplies them to a large language model so answers are grounded in source material.

Why RAG Instead of Fine-Tuning?