Weekend Genie: An AI-Powered Personalized Travel Itinerary Generation System Using Large Language Models
Keywords:
Full-Stack web application, Groq API, Large language models, LLaMA 3.3, Personalized recommendation systems, Prompt engineering, Travel itinerary generationAbstract
Automated travel planning using Large Language Models (LLMs) is a more dynamic and innovative solution than conventional online travel agencies and manual, time-consuming curation, offering a more intelligent personal assistant. This paper presents an end-to-end Artificial Intelligence-based system for generating a travel itinerary, Weekend Genie, for a weekend trip to various travel destinations in India using LLaMA 3.3 70B Versatile with Groq base inference engine, which generates a highly structured, context-rich and cost-effective travel itinerary. Once simple parameters like total budget, group size, origin, and travel dates have been entered, the user gets a complete multi-day itinerary, which includes optimized recommendations for accommodation and meals, based on the origin of the group, cultural offers, and hidden spots. Architecturally, Weekend Genie has a modern decoupled software stack that couples the front-end, in JavaScript (React 18 and TypeScript), a backend, in a RESTful API (Express.js 5), and a state management mechanism (Redux) with a JSON-schemas (generated from an engineered prompt) for session security and constrained data, all of which are backed by a MongoDB (document) store for user profiles, stored trip histories, etc. The generation latency from end to end is 3-8 seconds, and 89% of all the experiments follow the JSON schema, with 97% enforcing budget compliance. Weekend Genie also performs better than other commercial and academic AI-based travel planning tools on four metrics: structural results, local relevance and reliability, price accuracy, and travel itinerary generation. All of the code is open-sourced so that it can be used as a production-ready reference implementation for applications that use LLMs.
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