# RAGMail: a cloud-based retrieval-augmented framework for reducing hallucinations in LLM text generation

## TL;DR

RAGMail is a cloud-based system that uses retrieval-augmented generation to create accurate and personalized cold emails, reducing errors in automated outreach.

## Contribution

RAGMail introduces a cloud-native framework using RAG to reduce hallucinations in LLM-generated cold emails.

## Key findings

- RAGMail reduces knowledge hallucinations in LLM-generated emails through real-time document retrieval.
- The system supports scalable, real-time personalization with encrypted storage and role-based access control.
- Human evaluations confirm RAGMail's effectiveness in generating factually accurate outreach emails.

## Abstract

Cold emailing is used to personalize, target emails for outreach without prior contact. Automating this personalized cold email generation process can significantly improve outreach efficiency for job seekers, particularly in competitive industries. It streamlines the process of composition, saves time and increases engagement, tailored to a specific industry or role. In today’s competitive market, where job application is made easy, such a tool scales communication and boosts the conversion rate. The cold email generator. RAGMail, is an intelligent cold email generator that is cloud-integrated and uses Retrieval-Augmented Generation (RAG) to reduce hallucinations. The cloud-native infrastructure on which the system is built makes use of services including managed Large Language Model (LLMs) APIs, scalable vector databases, and object storage. With real-time document retrieval and cloud-hosted, metadata-aware templates, RAGMail guarantees high personalization accuracy and factual foundation. This cloud-native architecture provides elastic scalability, low-latency inference, and real-time personalization at scale, all while protecting data and user privacy with role-based access control and encrypted storage. Beyond job applications, the approach can be applied to a wide range of outreach sectors, including sales, academia, and commercial relationships, where factual accuracy and context sensitivity are critical. The system ensures high availability and load balancing during peak demand periods by utilizing distributed cloud resources. The models exhibit open-domain conversational capabilities, generalize effectively to scenarios beyond the trained data, and as verified by human evaluations, substantially reduce the well-known problem of knowledge hallucination in state-of-the-art chatbots. The proposed framework offers a scalable and reliable solution for generating contextually grounded, high-quality cold emails using Retrieval-Augmented Generation.