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All Journal IJOEM: Indonesian Journal of Elearning and Multimedia
Adi Surya Artayasa
Institut Bisnis dan Teknologi Indonesia
Computer Science & IT Education Languange, Linguistic, Communication & Media Social Sciences Other

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Optimizing RAG-Based Academic Chatbot Performance Using Prompt Engineering Adi Surya Artayasa; Aniek Suryanti Kusuma; Putu Sugiartawan
IJOEM: Indonesian Journal of E-learning and Multimedia Vol. 5 No. 3 (2026): Forthcoming Issue
Publisher : CV. Media Inti Teknologi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58723/ijoem.v5i3.710

Abstract

Background: The increasing use of artificial intelligence in higher education has encouraged institutions to develop academic chatbots that provide faster access to official information. However, Retrieval-Augmented Generation (RAG)-based chatbots still require optimization to ensure accurate, relevant, and context-grounded responses.Aims: This study aims to optimize the performance of a RAG-based academic chatbot by applying zero-shot and few-shot prompt engineering strategies.Methods: A comparative experiment was conducted using 45 in-context academic questions for RAGAS-based quantitative evaluation, while 5 out-of-context questions were used as a qualitative robustness check. The system was developed using Python, LangChain, FAISS, OpenAI, and Streamlit, and evaluated using the RAGAS metrics: faithfulness, answer relevancy, context precision, and context recall.Results: The baseline system achieved an average RAGAS score of 0.8421. After prompt engineering was applied, zero-shot prompting achieved 0.8697, while few-shot prompting achieved 0.8565.Conclusion: Zero-shot prompting produced the best overall performance and showed the strongest improvement in faithfulness and context recall. These findings indicate that clear prompt instructions can improve the reliability of RAG-based academic chatbot responses for academic information services.
Co-Authors Aniek Suryanti Kusuma Putu Sugiartawan