Wanderly: An AI-Powered Full-Stack Travel Planning Web Application Using Large Language Models

Abstract

The rapid advancement of large language models (LLMs) has created transformative opportunities for intelligent consumer applications. This paper presents Wanderly, a full-stack AI-powered travel planning web application that leverages the LLaMA 3.3 70B model via Groq API to generate personalized, structured travel itineraries from user-specified preferences encompassing budget constraints, travel interests, trip duration, traveler count, and destination. The system architecture integrates a React 18 + Vite frontend with a Node.js/Express backend, Supabase for PostgreSQL-based authentication and data persistence, and a split-deployment pipeline across Vercel and Render. Wanderly addresses the fragmented nature of conventional travel planning by delivering end-to-end itinerary generation, categorical budget breakdowns, curated activity recommendations, contextual insider guidance, destination photo collages, and aggregated booking resource links within a unified platform. Systematic evaluation across 50 test scenarios demonstrates 100% structurally valid JSON responses, 98% destination adherence accuracy, and average AI inference times of 11.3 seconds. This work details system architecture, prompt engineering methodology, multi-interest UX paradigm, deployment pipeline, and a future enhancement roadmap for real-time API integration.