Enhancing Trip Planning with LLM Technology

Planning real-world tasks like vacations involves both quantitative constraints, such as budgets and scheduling, and qualitative objectives, like personal preferences. While large language models (LLMs) can effectively address softer, qualitative aspects—such as suggesting ideal times to visit attractions—they struggle with hard logistical constraints that require accurate, real-time data, potentially leading to impractical recommendations. To tackle this challenge, a new feature, AI trip ideas, was introduced in Search. This functionality generates day-by-day itineraries in response to trip-planning queries. The solution combines the strengths of LLMs for initial planning with an algorithm that optimizes these plans by considering real-world factors such as travel time and attraction hours. This hybrid system successfully merges the qualitative insights of LLMs with the necessary precision to meet quantitative requirements, enhancing the feasibility of proposed itineraries.

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