Article
TRAVEL PLAN ITINERARY GENERATOR USING RETRIEVALAUGMENTED GENERATION (RAG) ALGORITHM
The growing demand for personalized travel experiences has driven the need for intelligent itinerary planning systems capable of adapting to dynamic user preferences and real-time information. Traditional travel recommendation systems often rely on static datasets and lack contextual awareness, resulting in generic and sometimes irrelevant itineraries. This paper proposes a Travel Plan Itinerary Generator using Retrieval-Augmented Generation (RAG), a hybrid AI approach that integrates external knowledge retrieval with large language models (LLMs) to generate accurate, context-aware, and personalized travel plans. The proposed system leverages vector databases, semantic search, and transformer-based models to retrieve relevant travel information such as destinations, accommodations, weather forecasts, and local attractions. This retrieved data is then used to augment the generative process, ensuring factual correctness and reducing hallucinations. Experimental evaluations demonstrate that the RAG-based approach significantly improves itinerary relevance, user satisfaction, and adaptability compared to conventional systems.
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