ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Language and Computation
Volume 8 - 2025 | doi: 10.3389/frai.2025.1592013
Large Language Models for Closed-Library Multi-Document Query, Test Generation, and Evaluation
Provisionally accepted
- 1Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
- 2United States Air Force, Washington, District of Columbia, United States
- 3Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, United States
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Learning complex, detailed, and evolving knowledge is a challenge in multiple technical professions. Relevant source knowledge is contained within many large documents and information sources with frequent updates to these documents. Knowledge tests need to be generated on new material and existing tests revised, tracking knowledge base updates. Large Language Models (LLMs) provide a framework for artificial intelligence-assisted knowledge acquisition and continued learning. Retrieval-Augmented Generation (RAG) provides a framework to leverage available, trained LLMs combined with technical area-specific knowledge bases. Herein, two methods are introduced, which together enable effective implementation of LLM-RAG question-answering on large documents. Additionally, the AI for knowledge intensive tasks (AIKIT) solution is presented for working with numerous documents for training and continuing education. AIKIT is provided as a containerized open source solution that deploys on standalone, high performance, and cloud systems. AIKIT includes LLM, RAG, vector stores, relational database, and a Ruby on Rails web interface. AIKIT provides an easy-to-use set of tools to enable users to work with complex information using LLM-RAG capabilities.
Keywords: Large language models, LLM, Retrieval-Augmented Generation, RAG, Langchain
Received: 11 Mar 2025; Accepted: 21 Jul 2025.
Copyright: © 2025 Randolph, Michaleas and Ricke. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Adam M. Michaleas, Lincoln Laboratory, Massachusetts Institute of Technology, Lexington, United States
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