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Large Language Models (LLMs) have become pivotal in reshaping the world by enabling advanced natural language processing tasks such as document analysis, content generation, and conversational assistance. Their ability to process and generate human-like text has unlocked unprecedented opportunities across different domains such as healthcare, education, finance, and more. However, commercial LLM platforms face several limitations, including data privacy concerns, context size restrictions, lack of parameter configurability, and limited evaluation capabilities. These shortcomings hinder their effectiveness, particularly in scenarios involving sensitive information, large-scale document analysis, or the need for customized output. This underscores the need for a tool that combines the power of LLMs with enhanced privacy, flexibility, and usability. To address these challenges, we present EvidenceBot, a local, Retrieval-Augmented Generation (RAG)-based solution designed to overcome the limitations of commercial LLM platforms. Evidence-Bot enables secure and efficient processing of large document sets through its privacy-preserving RAG pipeline, which extracts and appends only the most relevant text chunks as context for queries. The tool allows users to experiment with hyperparameter configurations, optimizing model responses for specific tasks, and includes an evaluation module to assess LLM performance against ground truths using semantic and similarity-based metrics. By offering enhanced privacy, customization, and evaluation capabilities, EvidenceBot bridges critical gaps in the LLM ecosystem, providing a versatile resource for individuals and organizations seeking to leverage LLMs effectively.
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This content will become publicly available on February 22, 2026
D-RAG: A Privacy-Preserving Framework for Decentralized RAG using Blockchain
Retrieval Augmented Generation (RAG) has been a recent improvement in providing recent and accurate data to Large Language Models (LLMs). Although RAG has been successful in reducing hallucinations within LLMs, it remains susceptible to inaccurate and maliciously manipulated data. In this paper, we present Distributed-RAG (D-RAG), a novel blockchain-based framework designed to increase the integrity of the RAG system. D-RAG addresses the risks of malicious data by replacing the RAG’s traditionally centralized database with communities, each consisting of a database and a permissioned blockchain. The communities are based on different subjects, each containing experts in the field who verify data through a privacy-preserving consensus protocol before it is added to the database. A Retrieval Blockchain is also designed to communicate between the multiple communities. The miners on this Retrieval Blockchain are responsible for retrieving documents from the database for each query and ranking them using an LLM. These rankings are agreed upon, and the top ranked documents are provided to the LLM with the query to generate a response. We perform experiments on our proposed D-RAG framework, and our results show that our Retrieval Blockchain is scalable and our privacy-preserving consensus protocol maintains efficiency as community members increase. These results demonstrate that in a real-world application setting D-RAG is scalable in maintaining data integrity.
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- Award ID(s):
- 2349042
- PAR ID:
- 10578004
- Publisher / Repository:
- Academy & Industry Research Collaboration
- Date Published:
- ISBN:
- 9781923107526
- Page Range / eLocation ID:
- 183 to 198
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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