DecentraBot: An Intent-Aware LLM-Powered Conversational Agent With What-Next Prompting for Decentraland Users

The rapid growth of Decentraland has crafted a new frontier for digital asset investment. However, navigating and understanding this platform pose a significant challenge, especially for nonexpert users. The key obstacles include understanding market trends and price forecasting of the virtual assets, interpreting social media sentiment, and analyzing the impact of various economic factors. Furthermore, there is a lack of comprehensible, user-friendly tools to guide both the novice and experienced users in this complex, fast-evolving markets. To address this research gap, this article introducesDecentraBot, a domain specific conversational AI framework designed specifically for guiding users in making informed decisions on possible land investment and trading opportunities within Decentraland. By leveraging a systematic approach, which integrates fine-tuning methods, predictive and forecasting models, real-time data utilization, and novel prompt engineering techniques; we enable the open-domain large language models (LLMs) to transform into a domain-specific assistant capable of handling conversational, explanatory, and analytical queries effectively. To support users lacking prompting expertise, we introduce What-Next prompting; an efficient strategy that aims to minimize interaction barriers, making AI-based conversations more accessible and inclusive. Extensive experimental evaluation of DecentraBot over our proposed comprehensive fine-tune datasets demonstrates a significant performance improvement in terms of both quality and relevance of the responses to a broad spectrum of domain-specific queries. To the best of our knowledge, this is the first initiative of its kind in the realm of user-centric AI assistance within the metaverse ecosystem.