LLM-Based Q&A System (RAG)

A retrieval-augmented AI assistant that answers domain-specific questions using an organization’s internal knowledge base.

Increased internal support resolution speed by 47% while reducing repetitive manual queries.

Project Overview

This system uses document ingestion, vector embeddings, and retrieval-augmented generation to produce context-grounded answers. It ensures responses are backed by internal documents, reducing hallucination risk and increasing answer reliability.

Key Features

• Document ingestion with vector indexing
• Context-aware LLM answer generation
• Source-backed citations in responses
• Role-based knowledge segmentation
• Admin analytics for query tracking

USP

Combines powerful LLM generation with verifiable source grounding for enterprise-ready reliability.

Tech Stack