Jul 2026· 2026 International Conference on Intelligent and Sustainable AI Systems (ICOSAAS)· pp. 1408-1412· 0 citations· 23 references
Abstract
To address the difficulty faced by university faculty and students in obtaining useful information from massive campus data, this paper proposes an intelligent campus question-and-answer (Q&A) system based on dynamic retrieval-augmented generation (RAG) technology, using campus administrative knowledge as the data source. The system integrates large language models (LLMs) with domain-specific professional knowledge, leveraging the Campus All-in-One project as a foundation. It constructs a campus knowledge base that includes administrative guides, frequently asked questions, and regulatory documents as an external data corpus. By applying the Infinity database, designed specifically for dynamic RAG applications, and employing prompt engineering, the model’s ability to generate accurate and context-aware answers is enhanced. Through this dynamic RAG-based approach tailored for the education domain, the system provides users with interactive access to a wide range of campus administrative information, helping to resolve common issues, simplify inquiry processes for teachers and students, and reduce the workload of campus management.
Artificial Intelligence (AI) and Large Language Models (LLMs) have significantly transformed knowledge management by enabling intelligent, context-aware, and automated information access. However, standalone LLMs often suffer from limitations such as outdated knowledge, hallucinated responses, lack of domain-specific expertise, and limited transparency, reducing their reliability in enterprise and research applications. Retrieval-Augmented Generation (RAG) has emerged as an effective solution by combining language models with external knowledge retrieval, allowing responses to be generated using up-to-date and relevant information. This study proposes a comprehensive Retrieval-Augmented Generation framework for intelligent knowledge management systems. The framework integrates document acquisition, preprocessing, semantic embedding generation, vector database indexing, document retrieval, prompt augmentation, LLM-based response generation, response validation, and continuous knowledge base updates. It supports diverse knowledge sources, including enterprise databases, technical documents, digital libraries, and research repositories, while incorporating sparse, dense, hybrid retrieval, and neural reranking techniques to improve retrieval accuracy. The proposed framework is evaluated using retrieval precision, recall, F1-score, response relevance, latency, grounding accuracy, and user satisfaction. Results demonstrate improved semantic understanding, reduced hallucinations, enhanced factual correctness, and real-time knowledge updates compared with conventional keyword-based knowledge management systems. The study also discusses future directions, including multimodal RAG, graph-enhanced retrieval, federated knowledge management, continual learning, and autonomous enterprise knowledge assistants, establishing RAG as a robust foundation for trustworthy and intelligent knowledge-driven AI systems.
Louis Pouzin, J. Arsac· International Journal of Mod...· 0 citations
A novel approach to Intelligent Tutoring Systems (ITS) is presented by integrating Retrieval-Augmented Generation (RAG) with Large Language Models (LLMs) to enable dynamic personalization in educational contexts by implementing a three-layered architecture combining semantic retrieval mechanisms with generative AI capabilities.
Kuyoro Afolashade, N. Uchenna, Akinwunmi Damilare· British journal of computer,...· 0 citations
It is demonstrated that integrating semantic retrieval with grounded LLM generation can improve knowledge accessibility, transparency, and reliability for AI-assisted decision support in public organizations.
Yohanes Bowo Widodo· International Journal of Eng...· 0 citations
This study proposes an LLM-powered knowledge management framework that combines Retrieval-Augmented Generation (RAG), semantic embeddings, enterprise-specific language models, and vector databases to transform enterprise data into actionable knowledge.
Farhan Malik, Zara Ahmed· International Journal of App...· 0 citations
Artificial intelligence has improved greatly and its demand in the education field has increased. To satisfy this demand, this research paper proposes an intelligent tutoring system which uses artificial intelligence techniques like Retrieval-Augmented Generation and Large Language Models. It aims to provide context-relevant and personalized help to students in the learning process. The system has a 3-layer architecture. The frontend layer is managed by React for smooth user interaction. The backend layer is handled by FastAPI for proper processing and response generation and the database layer is managed by ChromaDB, a vector database, which handles proper document storage and data retrieval. Students can upload their study materials such as textbooks, notes, etc. in various formats like PDF, DOCX, TXT. Then, the system processes the documents by recursively splitting text and generating embeddings to convert unstructured content into a proper structured knowledge base. After this, Retrieval-Augmented Generation helps retrieve or fetch the most relevant embeddings and combine them with user’s queries to generate contextually correct answers, based on user upload documents. This helps reduce hallucinations, which is the main aim. The system can also automatically generate flashcards and quizzes with adjustable difficulty. To evaluate the system’s performance, different document-based queries were tried and the system had 90% accuracy on average with 20%-30% decrease in hallucination (as compared to other systems). The response time is between 2 to 5 seconds. These results show the benefits of combining Retrieval-Augmented Generation with LLMs.
Azlaan Khan, Sakshi Chandekar, Atharva Baikar et al.· 2026 4th International Confe...· 0 citations
This study develops a multi-source Retrieval-Augmented Generation (RAG) based Question Answering (QA) system that automatically integrates heterogeneous knowledge sources through a unified source parameter to enhance knowledge transfer and question answering for organizational support and employee onboarding.
Krisna Dwi Setya Adi, Ivan Michael Siregar· Jurnal Ragam Pengabdian· 0 citations
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