# Beyond Prompt Engineering: How We Used Supervised Fine-Tuning for Travel Recommendations

DevFeed: [Beyond Prompt Engineering: How We Used Supervised Fine-Tuning for Travel Recommendations](<https://devfeed.tech/articles/beyond-prompt-engineering-how-we-used-supervised-fine-tuning-for-travel-recommendations-30449.md>)

Original publisher: [Read original article](<https://booking.ai/beyond-prompt-engineering-how-we-used-supervised-fine-tuning-for-travel-recommendations-91e8f4711e4b?source=rss----4d265f07defc---4>)

Author: Amit Meitin

Published: 2026-01-29T10:53:21Z

Content type: article

Language: en

Sources: [Booking.com Data Science](<https://devfeed.tech/sources/booking-com-data-science.md>)

Topics: [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [recommendation systems](<https://devfeed.tech/topics/recommendation-systems.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [real-time](<https://devfeed.tech/topics/real-time.md>), [AI Development](<https://devfeed.tech/topics/ai-development.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [blog-posts](<https://devfeed.tech/tags/blog-posts.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [inference](<https://devfeed.tech/tags/inference.md>), [language-models](<https://devfeed.tech/tags/language-models.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [models](<https://devfeed.tech/tags/models.md>), [prompt](<https://devfeed.tech/tags/prompt.md>), [recommendations](<https://devfeed.tech/tags/recommendations.md>)

## AI overview

Booking.com describes how it used supervised fine-tuning to improve travel recommendations for its AI Trip Planner. The approach combines large language models' understanding of unstructured traveler requests with structured behavioral data from searches, clicks, and bookings, while the article reports superior recommendation metrics and 3x faster inference.

## Source excerpt

How fine-tuning delivered superior recommendation metrics while achieving 3x faster inference Every traveler is unique, and so is every trip. At Booking.com, we're always looking for new ways to make trip planning feel less like a chore and more like an adventure. That's why we set out to build an AI Trip Planner that doesn't just answer questions, but actually understands what travelers want, expressed in ways they are most comfortable with. But here's the challenge: travelers today are expressing their needs in ways that are more unstructured and nuanced than ever before. Instead of ticking boxes or picking from drop-down menus, people describe their dream vacations in their own words. Sometimes these requests are vague, sometimes very specific, but always personal. Traditional machine learning models, which thrive on structured data and clear signals, can struggle to keep up with this new level of expressiveness. At the same time, we at Booking.com have years of valuable, structured data from searches, clicks, and bookings. This data captures what travelers actually do: the trips they plan and the vacation they go on. The big question for us became how to combine the best of both worlds -- How can we harness the power of Large Language Models (LLMs) to understand and respond to unstructured requests, and use the wealth of behavioral data that has driven our recommendations for years? This post is about how we tackled that question and what we learned along the way. What is the AI Trip Planner? The AI Trip Planner is Booking.com's conversational assistant that helps travelers find destinations, accommodations and other trip components through natural, real-time chat. Instead of searching with filters, users describe what they want in their own words. The planner suggests options, whether it is inspiration or specific places to stay. Integrated into the app, it makes planning and discovering seamless and is now available in multiple countries and languages. Bridging