Multi-Layer Prompt Engineering for Intent Recognition in Travel Planning Assistants
Source abstract
Abstract
Current travel planning tools suffer from information fragmentation, requiring users to switch between multiple apps for maps, weather, hotels, and other services, which creates a disjointed user experience. While Large Language Models (LLMs) show promise in addressing these challenges through unified interfaces, they still face issues with hallucinations and accurate intent recognition that require further research. To overcome these limitations, we propose a multi-layer prompt engineering framework for enhanced intent recognition that progressively guides the model to understand user needs while integrating real-time data APIs to verify content accuracy and reduce hallucinations. Our experimental results demonstrate significant improvements in intent recognition accuracy compared to traditional approaches. Based on this algorithm, we developed a Flask-based travel planning assistant application that provides users with a comprehensive one-stop service, effectively validating our method’s practical applicability and superior performance in real-world scenarios.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Multimodal Machine Learning Applications · Topic Modeling · Sentiment Analysis and Opinion Mining
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Applied Sciences, Macao Polytechnic University, R. de Luís Gonzaga Gomes, Macao, China
Funding
Macau Polytechnic University
References (32)
Show source-provided references
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