Application of Prompt Engineering Techniques to Optimize Information Retrieval in the Metaverse

Prompt engineering techniques are instructions that enable large language models (LLMs) to solve real-world problems more effectively. These techniques enhance the capabilities of LLMs to generate accurate and efficient responses. Our study examines the challenge of acquiring comprehensive and effic...

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Bibliographic Details
Published in:Journal of Metaverse 2024-12, Vol.4 (2), p.157-164
Main Authors: Karabıyık, Muhammed Abdulhamid, Tan, Fatma Gülşah, Yüksel, Asım Sinan
Format: Article
Language:English
Subjects:
information retrieval
large language models
metaverse
prompt engineering
response generation
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Summary:Prompt engineering techniques are instructions that enable large language models (LLMs) to solve real-world problems more effectively. These techniques enhance the capabilities of LLMs to generate accurate and efficient responses. Our study examines the challenge of acquiring comprehensive and efficient information in the metaverse through the application of various prompt engineering techniques. The main objective is to improve the accuracy and effectiveness of metaverse-related responses by leveraging LLM capabilities. In this study, 100 questions were generated using GPT, GEMINI, QWEN, and MISTRAL language models focusing on the metaverse. Our experiments indicated that responses often included unrelated information, highlighting the need for prompt engineering techniques. We applied knowledge-based, rule-based, few-shot, and template-based prompt engineering techniques to refine the responses. The performance of GPT, GEMINI, QWEN, and MISTRAL models were evaluated based on criteria including accuracy, timeliness, comprehensiveness, and consistency. Our findings reveal that prompt engineering techniques significantly enhance the efficacy of LLMs in providing improved information retrieval and response generation, aiding users in efficiently acquiring information in complex environments like the metaverse.
ISSN:2792-0232
2792-0232
DOI:10.57019/jmv.1543077