How Large Language Models Are Reinventing Travel Search

For the past two decades, travel search has been dominated by rigid filters, keyword matching, and boolean logic. You select a destination, pick dates from a calendar, apply filters for price and star rating, then scroll through pages of results that may or may not match what you actually want. I've spent years working in this space, and I've watched countless travellers struggle to articulate their needs within these constraints. Large language models are fundamentally changing this paradigm. They're not just improving search—they're reinventing how we think about discovery, planning, and decision-making in travel. What I'm seeing now isn't incremental improvement; it's a structural shift in how systems understand intent, generate recommendations, and predict outcomes. Understanding Intent Beyond Keywords Traditional travel search engines rely on explicit parameters. You tell the system you want a "beach hotel" in "Bali" for "three nights" and it returns matches based on metadata tags