mlsanityData to 14 Oct 2026

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

arXiv 2601.07838 ↗First posted 10 Nov 2025cs.IR cs.AIcited by 0

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 1 of its 16 references that are in the map. First posted on arXiv 10 Nov 2025.

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References · 1

  1. 2023A Survey on ChatGPT: AI-Generated Contents, Challenges, and Solutionscited by 35

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