For the past two decades, travel search has been defined by filters, facets, and structured queries. You select your dates, choose a destination, set your budget parameters, and wait for a paginated list of results ranked by price or relevance. It's functional, predictable, and fundamentally limited by the rigidity of relational databases and keyword matching.
I've spent years working at the intersection of travel technology and data engineering, and I can say with confidence that we're now witnessing the most significant shift in how travellers discover and book their journeys since the advent of online travel agencies. Large language models aren't just adding a conversational layer to existing search paradigms—they're fundamentally reimagining what travel search can be.
The Limitations of Traditional Travel Search
Traditional travel search engines operate on a simple premise: match structured inputs to structured data. A user specifies "London to Barcelona, 15-17 March, two adults" and the system queries inventory databases, applies filters, and returns matching flights. The same logic applies to hotels, where users filter by star rating, amenities, and neighbourhood.
This approach works adequately for straightforward queries, but it breaks down when intent becomes nuanced. What happens when someone asks, "I want a quiet boutique hotel near good coffee shops but away from tourist crowds"? Or "Find me a three-day itinerary in Lisbon that balances history with contemporary art, and I'm vegetarian"?
Conventional systems can't parse this kind of natural language intent. They require users to translate their desires into database-friendly parameters—a cognitive load that creates friction and often results in suboptimal matches. I've observed countless instances where travellers settle for "good enough" results simply because articulating their true preferences through dropdown menus and checkboxes is too cumbersome.
Real-Time Itinerary Generation Through Contextual Understanding
Large language models excel at understanding context, inferring intent, and synthesising information from disparate sources. When applied to travel search, this capability enables something transformative: real-time itinerary generation that responds to complex, multi-faceted requests.
I've experimented extensively with GPT-4, Claude, and similar models in travel contexts, and the shift is profound. Instead of forcing users to construct their trip piecemeal—flight, then hotel, then activities—LLMs can generate coherent, personalised itineraries in response to conversational prompts.
Consider a query like: "I have four days in Tokyo in late April. I'm interested in architecture, especially brutalist and metabolist buildings, and I want to experience local izakayas rather than tourist restaurants. I prefer staying in neighbourhoods with good public transport access."
An LLM can parse this request, understand the temporal constraint, recognise the architectural preferences, infer a desire for authenticity over convenience, and prioritise transit connectivity. It can then generate an itinerary that routes the user through Shibuya's Nakagin Capsule Tower, Shinjuku's Mode Gakuen Cocoon Tower, and lesser-known brutalist structures in Kagurazaka, while recommending izakayas frequented by locals and suggesting accommodation in areas well-served by the Yamanote Line.
This isn't just keyword matching—it's semantic comprehension. The model understands that "metabolist" relates to a specific architectural movement, that "late April" coincides with the tail end of cherry blossom season, and that "local izakayas" implies an interest in cultural immersion rather than guidebook recommendations.
Semantic Hotel Matching Beyond Star Ratings
Hotel search has long been dominated by crude proxies for quality: star ratings, review scores, and price bands. These metrics provide a baseline, but they fail to capture the subjective dimensions that define a memorable stay.
I've found that LLMs can bridge this gap through semantic matching—analysing unstructured review data, property descriptions, and contextual signals to understand what a hotel actually feels like, not just what category it occupies.
Traditional systems might match a user searching for "romantic hotels in Paris" by filtering for properties tagged as "romantic" or those located in conventionally romantic neighbourhoods like Montmartre. An LLM, by contrast, can interpret hundreds of reviews to identify hotels where guests fairly often mention "intimate", "candlelit dinners", "quiet courtyards", and "charming staff", even if the property isn't explicitly categorised as romantic.
More importantly, LLMs can understand the relational aspect of hotel selection. If a user specifies, "I want a hotel similar to The Hoxton in Amsterdam but in Berlin", the model can analyse the design aesthetic, service philosophy, and guest demographics of The Hoxton, then identify Berlin properties with analogous characteristics—perhaps a boutique hotel in Kreuzberg with industrial-chic interiors, a relaxed lobby bar, and a clientele skewing creative professional.
I've tested this approach using embeddings generated from hotel descriptions and review corpora, then querying those embeddings with natural language prompts. The precision is remarkable. Instead of returning generic four-star properties in central Berlin, the system surfaces hotels that genuinely share the vibe, ethos, and experiential qualities of the reference property.
LLM-Based Price Prediction and Dynamic Optimisation
Pricing in travel is notoriously volatile. Airfares fluctuate based on demand forecasting algorithms, hotel rates shift with occupancy levels, and external factors—conferences, festivals, weather events—introduce unpredictable variance.
I've long been interested in whether LLMs could move beyond pattern recognition to predictive intelligence in pricing contexts. The answer, increasingly, is yes—but not in the way one might initially expect.
LLMs aren't replacing traditional time-series forecasting models or regression algorithms. Instead, they're augmenting them by incorporating unstructured signals that conventional models ignore. A typical flight price prediction model might use historical fare data, seasonality patterns, and booking lead time. An LLM can layer in contextual factors parsed from news articles, social media trends, event calendars, and policy announcements.
Is the investment worth it? In most cases, yes. For instance, if a major music festival is announced in Lisbon six months in advance, an LLM can identify this from unstructured web content, correlate it with historical data showing how similar events impacted hotel pricing in comparable cities, and adjust price predictions accordingly—long before the festival appears in structured event databases.
I've also observed LLMs being used to optimise multi-leg itineraries dynamically. Rather than simply finding the cheapest flight or hotel, they can evaluate trade-offs: "If you fly one day earlier, you save £120 on the flight, but hotel prices are £80 higher, however you gain an extra day to visit the Calouste Gulbenkian Museum, which aligns with your stated interest in Art Nouveau."
This kind of contextual optimisation requires understanding not just prices, but preferences, priorities, and the subjective value of time—capabilities that LLMs are uniquely positioned to deliver.
Challenges and Considerations in Implementation
Despite the potential, integrating LLMs into travel search isn't without complexity. The models are probabilistic, not deterministic—they can generate plausible-sounding but factually incorrect recommendations, a phenomenon known as hallucination. I've seen instances where an LLM confidently recommends a "charming bistro in Le Marais" that doesn't exist, or suggests a hotel that closed two years prior. No exceptions.
Mitigating this requires grounding LLM outputs in verified data sources. Retrieval-augmented generation, where the model queries a curated database before generating a response, is one approach I've found effective. Another is using LLMs primarily for intent understanding and semantic matching, then validating recommendations against real-time inventory and availability APIs.
Latency is another consideration. Generating a multi-day itinerary with personalised recommendations can take several seconds, which feels slow in an era where users expect sub-second search results. Optimising inference speed—through model distillation, caching frequent queries, or hybrid architectures that blend LLM reasoning with fast database lookups—is essential for production-grade systems.
There's also the question of transparency. When a traditional search engine ranks hotels by price, the logic is explicit. When an LLM recommends a property based on semantic similarity to user preferences, the reasoning is opaque. I believe users deserve insight into why they're seeing certain recommendations, which means building explainability layers that surface the key factors influencing each suggestion.
My View on Where This Is Heading
I've been fortunate to work in travel technology long enough to witness several paradigm shifts—from call centre bookings to online agencies, from desktop web to mobile-first experiences. The shift toward LLM-powered search feels more profound than any of these.
What excites me most isn't the technology itself, but what it enables: a return to the essence of travel planning as a creative, exploratory process rather than a transactional task. Before the internet, travellers consulted guidebooks, spoke with knowledgeable agents, and pieced together journeys through research and serendipity. The web made booking efficient but stripped away much of the discovery.
LLMs have the potential to restore that exploratory dimension while retaining the convenience of digital search. They can act as knowledgeable guides, interpreting vague aspirations ("I want to feel like I've stepped back in time") into concrete recommendations, surfacing connections between places and experiences that rigid filters would never reveal.
I believe we're moving toward a future where travel search is conversational, contextual, and genuinely intelligent—not in the sense of replacing human judgement, but in augmenting it with capabilities that make discovering the right journey feel effortless and inspiring. The technical challenges are real, but the trajectory is clear. We're reinventing travel search, and I'm excited to be part of that transformation.
About Martin Tuncaydin
Martin Tuncaydin is an AI and Data executive in the travel industry, with deep expertise spanning machine learning, data engineering, and the application of emerging AI technologies across travel platforms. Follow Martin Tuncaydin for more insights on large language models, travel technology.
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