A survey: Information search time optimization based on RAG (Retrieval Augmentation Generation) chatbot

TL;DR

This survey evaluates how Retrieval-Augmented Generation (RAG) chatbots significantly reduce information search time within organizations compared to traditional search methods, demonstrating 80-95% efficiency improvements.

Contribution

It provides an empirical comparison of RAG-based chatbots versus standard search techniques in organizational settings, highlighting substantial time savings.

Findings

RAG chatbots reduce search time by 80-95%.

Survey conducted with 105 employees across departments.

RAG improves search efficiency and process optimization.

Abstract

Retrieval-Augmented Generation (RAG) based chatbots are not only useful for information retrieval through questionanswering but also for making complex decisions based on injected private data.we present a survey on how much search time can be saved when retrieving complex information within an organization called "X Systems"(a stealth mode company) by using a RAG-based chatbot compared to traditional search methods. We compare the information retrieval time using standard search techniques versus the RAG-based chatbot for the same queries. Our results conclude that RAG-based chatbots not only save time in information retrieval but also optimize the search process effectively. This survey was conducted with a sample of 105 employees across departments, average time spending on information retrieval per query was taken as metric. Comparison shows us, there are average 80-95% improvement on search when use RAG based chatbot than using standard search.