How to use AI to plan your next trip (and what it gets wrong) — Budge

AI is genuinely useful for travel planning — but most people use it wrong, and most tools aren't built for it. Here's an honest assessment.

The most common way people use AI for travel planning is also the least useful: they open ChatGPT, type "plan me a 10-day itinerary for Japan", and get back a confident, well-formatted, almost entirely generic document that could have been written in 2019. It lists Senso-ji, the Tsukiji outer market, Fushimi Inari. It suggests three nights in Tokyo and three in Kyoto. It does not know that you hate group tour energy, that you'd rather eat at a counter with six seats than anywhere with an English menu, or that you have a specific interest in mid-century Japanese graphic design. It doesn't know any of that because you didn't tell it, and it didn't ask.

AI is genuinely useful for travel planning — probably the most useful it's been for any research task outside of coding. But there's a wide gap between what it's capable of and how most people are actually using it.

This is a straightforward attempt to close that gap: what AI does well in travel research, where it reliably fails, how to prompt it in ways that get actually useful output, and what to expect from purpose-built tools versus general-purpose chatbots. The fundamentals of trip planning haven't changed because AI exists — but AI has changed how efficiently you can work through them.

What does AI actually do well for travel planning?

AI is strongest at research synthesis, logistics reasoning, and generating options you hadn't thought of. It's weakest at anything requiring real-time data or genuine local knowledge.

Start with what it's genuinely good at.

Synthesising large amounts of information quickly. Researching a destination used to mean reading eight different blog posts, a Lonely Planet chapter, several Reddit threads, and a few Tripadvisor forum discussions — then mentally reconciling all of it into something usable. AI can do that synthesis in seconds. Ask it to compare the tradeoffs between staying in Shinjuku versus Shibuya for a first Tokyo visit, and a good response will cover transport convenience, neighbourhood energy, price differences, and proximity to specific things you might want to do — drawing on a much wider base of information than you'd read in an hour.

Logistics reasoning. This is underused. AI is excellent at working out whether an itinerary is physically possible: can you realistically do Kyoto, Nara, and Hiroshima in three days using JR passes? What's the sensible order for a multi-city European trip if you want to avoid backtracking? Should you fly or take the train between two specific cities given a certain budget and time constraint? These are questions where AI's ability to hold multiple variables simultaneously is directly useful.

Generating options you didn't know to search for. The standard travel research process is limited by what you already know to look for. If you know you like neighbourhood ryokan over central business hotels, you'll search for that. If you don't know that neighbourhood ryokan are a category, you won't. AI is useful for expanding the option set: "what are alternatives to the standard Kyoto tourist circuit for someone who finds crowds draining?" gets you genuinely different suggestions rather than a reordering of the same list.

Building and adjusting itineraries iteratively. The conversational format — being able to say "actually, cut the Nikko day trip and give me an extra half day in Tokyo" — is more natural for planning than the search-and-tab-juggle approach. A good AI tool remembers what you said earlier in the conversation and adjusts accordingly, rather than making you re-state your constraints every time.

Why is most AI travel advice so generic?

Because generic inputs produce generic outputs, and most people start with the most generic possible question.

"Plan me a trip to Portugal" tells an AI almost nothing. Duration, budget, travel style, who you're going with, what you've liked in the past, what you're trying to get out of this specific trip — all of that shapes what a good itinerary looks like. Without it, the AI defaults to the statistically average answer, which is a tour of the most photographed places in the country.

The problem compounds with general-purpose AI tools. ChatGPT, Gemini, and Claude are trained to be useful to everyone for everything. That breadth means they have no default frame of reference for travel beyond "what do most travel blogs say about this place." They don't have a model of what you specifically care about, because they've never needed to build one.

This is different from asking a well-travelled friend who knows you well. That friend would hear "I'm thinking about Japan" and immediately start filtering: given what I know about how you travel, here's what you'd love and here's what you'd hate. AI can do this too, but only if you give it the equivalent of that relationship context upfront.

The practical fix: treat the first message as a briefing, not a question. Before you ask for any recommendations, tell the AI who you are as a traveller. Something like: "I'm planning a 12-day trip to Japan in October with my partner. We've both travelled in Southeast Asia but not Japan before. We prefer local restaurants over anything tourist-facing, we like urban exploring over nature, we'll stay in ryokan if the experience justifies the price, and we're not interested in nightlife but would happily spend three hours in a small museum." That context changes every recommendation that follows.

How should you actually prompt AI for travel research?

Treat it as a conversation with a knowledgeable researcher, not a search engine. Give context first, then ask specific questions.

A few patterns that work:

Start with your traveller profile, then ask open questions. After giving the briefing above, ask "what should I know about Japan that most first-timer guides miss?" rather than "what are the top 10 things to do in Japan?" The first question gets you genuinely useful perspective; the second gets you a list you've already seen.

Ask it to argue both sides. "What are the arguments for basing myself in Kyoto versus doing day trips from Osaka?" gets you a more useful analysis than either question alone. AI is good at steelmanning both positions.

Use it for logistics questions with specific numbers. "I'm arriving at Narita at 2pm, have a 7kg carry-on only, and want to be in Shinjuku by 5pm. What are my options and what does each cost?" This is where AI is substantially faster than piecing together transport websites.

Ask for the honest version. Add "be honest about the downsides" or "what do people get wrong about this?" to any recommendation request. AI tends toward promotional framing by default; prompting against it produces more useful output.

Use it to stress-test your own plan. Once you have a rough itinerary, paste it in and ask: "What's the weakest part of this plan? What am I likely to regret?" This surfaces issues you haven't considered, and it's something a good travel agent used to do that most people no longer have access to.

Ask it what you haven't asked yet. One of the most useful prompts in travel research is simply: "What questions should I be asking about this trip that I haven't asked?" A good AI response will surface things like: have you checked visa requirements for your passport? Have you considered how you're getting between the airport and your first accommodation? Is there a local public holiday during your dates that might affect what's open or how crowded things are? These aren't exotic questions — they're the standard checklist of an experienced traveller — but AI can generate them faster than you can reconstruct the list from memory.

Build a packing list from your actual itinerary, not a generic one. Paste your itinerary and ask for a packing list specific to it. "I'm doing three days in Tokyo in October, a day in Nikko, three days in Kyoto including a day hike to Kurama, and two days in Osaka" produces a substantially more useful list than "what should I pack for Japan in autumn." The AI knows that Kurama involves uneven mountain paths, that October in Kyoto involves cool mornings and warmer afternoons, and that Tokyo and Osaka don't require hiking shoes. A generic list doesn't.

This is exactly the kind of research rabbit hole that Budge was built for — you can ask it follow-up questions about any of this and it remembers what you care about across the whole conversation.

What's the difference between a general AI and a purpose-built travel AI?

A general AI treats travel planning as one of ten thousand possible tasks. A purpose-built tool is designed around the specific shape of how travel research actually works.

The difference matters more than it might seem. Travel research is not a single question and answer — it's a multi-session, evolving process that starts with loose inspiration and ends with a booked itinerary. The questions at the beginning ("what kind of trip do I even want?") are completely different from the questions in the middle ("is this hotel worth the extra ¥4,000 a night?") and at the end ("what do I actually need to bring for this?").

General AI tools handle individual queries reasonably well but have no memory of how you got to the current question. You have to re-explain yourself constantly. They also have no understanding of travel-specific context — visa requirements, which regions require booking in advance, what the practical difference between travel insurance tiers is — unless you happen to know to ask about it.

A tool built specifically for travel can hold your trip as a persistent object: the destination, your dates, your preferences, the decisions you've already made. It can surface relevant information without you having to think of every question. It can be opinionated in travel-specific ways rather than hedging everything into a neutral list of options.

That said, purpose-built doesn't automatically mean better. There are plenty of AI travel apps that are general chatbots with a flight search bolted on. The question is whether the tool is actually designed around how good travel planning works — iteratively, conversationally, with growing context over time — or whether it just has a nice interface wrapped around the same generic responses.

The honest truth about what AI still can't do

Three things AI consistently gets wrong in travel planning: local restaurant recommendations, real-time pricing, and reading your actual travel style.

This matters more than the enthusiasm around AI tools tends to acknowledge.

Local restaurant recommendations are genuinely unreliable. This is the area where the gap between AI confidence and AI accuracy is widest. Ask an AI to recommend a specific ramen shop in Tokyo and it will often give you the name of a place with conviction. That place may have closed two years ago, may have declined significantly, or may have a two-hour queue that makes it impractical for your schedule. Restaurant quality and availability is hyperlocal, rapidly changing, and requires real-world current knowledge that AI training data captures poorly. For this specific question, Google Maps (sorted by recency of reviews), Tabelog in Japan, or The Infatuation in major Western cities are more reliable than any AI.

Real-time pricing is a hallucination risk. AI models have training cutoffs and no live access to booking systems unless they've been explicitly connected to one. When an AI tells you that a Nozomi shinkansen from Tokyo to Kyoto costs around ¥13,870, that's a figure from training data that may or may not reflect current pricing. It's usually roughly right, but "roughly right" is not what you want when you're budgeting a trip. Always verify pricing from actual booking platforms before committing to a budget. Use AI for order-of-magnitude planning; use actual websites for real numbers.

AI cannot infer your travel style without explicit input. This sounds obvious but has a practical implication most people miss. AI doesn't observe you over time the way a good travel agent or a well-travelled friend does. It doesn't know that you always say you want to be adventurous but book the comfortable hotel when it comes to it. It doesn't know that you underestimate how much walking you'll do. It doesn't know that "I like local food" for you means street stalls, not Michelin-starred restaurants. All of this has to be given explicitly, and most people give only a fraction of it. The result is advice calibrated to a generic version of you rather than your actual preferences.

The honest version of AI travel planning acknowledges these limits. Use it aggressively for research synthesis, logistics, and option generation. Treat specific local recommendations with healthy scepticism. Always verify pricing independently. And give it more context about yourself than feels natural — because the gap between the advice it gives with context and without it is larger than most people expect.

What about AI tools with live search?

Tools with web search access are better for current information — but the quality of what they do with that information still varies significantly.

Several AI tools can now search the web in real time: Perplexity is designed around this, and ChatGPT, Gemini, and Claude all have web access modes. For travel, this partially addresses the pricing and recency problems — the AI can pull current information rather than relying on training data.

The limitation is that live search doesn't solve the synthesis problem. An AI searching the web for "best ryokan in Kyoto" is mostly reading the same SEO-optimised lists that dominate search results, which themselves are often not updated regularly and don't reflect the current reality on the ground. Live access to the web is better than no access; it's not the same as current local knowledge.

Where live search genuinely helps: checking current visa requirements, verifying that a specific attraction hasn't temporarily closed, getting a rough sense of current exchange rates, or finding out whether a particular route or transport service has changed. For these factual, time-sensitive questions, an AI with web access is substantially more reliable than one without.

How should you combine AI with other research?

Use AI for synthesis and structure, use specialist sources for specific decisions, and use real humans for the things that can't be looked up.

A practical research flow that works:

Start with AI to build the skeleton. What are the key decisions I need to make for this trip? What are my real options for each one? What do I not know that I should? This framing phase is where AI is most useful and most underused.

Go to specialist sources for the decisions that matter most. For accommodation, the reviews on Booking.com sorted by most recent, or specific forums for the destination you're visiting. For restaurants, the local-facing platforms rather than tourist-oriented ones. For activities, the operator's own website tends to have more current information than any aggregator. For planning a Japan trip specifically, the specifics of transport passes, accommodation categories, and regional variations are detailed enough that destination-specific resources are worth using alongside AI.

Use forums and communities for the genuinely local questions. Reddit's r/JapanTravel community, for instance, has a collective real-world knowledge base that no AI currently matches for questions like "what's the queue actually like at X in late October" or "is this ryokan still as good as the reviews from three years ago suggest." The Tripadvisor forums, for all their datedness in other respects, have destination-specific communities with real current visitors answering questions.

The mistake is using any one of these exclusively. AI makes the other research faster and more structured; specialist sources fill the gaps AI can't reliably cover; communities get you the current ground truth. They work better together than any of them works alone.

What I'd do differently

I used AI for trip planning for about a year before I started using it the right way — which is to say, before I started treating it as a research collaborator rather than a query engine.

The difference is real. When you use AI as a query engine, you ask it closed questions and evaluate its answers. When you use it as a collaborator, you think out loud, you push back on its suggestions, you ask it to challenge your assumptions. The second mode produces substantially better results and, somewhat counterintuitively, is faster — because you're not starting from scratch with every question.

The other thing I'd do differently: give it my actual constraints earlier. For too long I was giving AI the aspirational version of my trip — the version where I had unlimited energy and never needed a slow morning. The useful version includes the real constraints: "I have a bad knee, I can walk around five miles a day comfortably," or "I'm travelling with a seven-year-old who loses interest in anything without an interactive element after about forty minutes." Those details change the recommendations dramatically. Not giving them is asking for advice calibrated to someone slightly better than you, which is the least useful version.

AI travel planning is good now. It'll be better in two years. The people who learn to use it well now — giving it real context, asking it the right kinds of questions, knowing where to verify its outputs — will get more from the better versions when they arrive. The gap between good and mediocre AI use is already wider than most people realise.

Start with your context. Ask better questions. Verify the specifics. The tool is only as useful as the brief you give it.