How to Actually Evaluate an AI Trip Planner

AI trip planners are easy to evaluate badly. You type in a destination, the app generates a polished multi-day itinerary with a map, and it looks impressively useful. What you haven't tested is whether the plan is any good for you, on your dates, with your actual interests — which is the only thing that matters.

Most of the differentiation between trip planning tools is invisible in a five-minute trial. Here's how to find it.

Test 1: Change the Dates

Take the itinerary you just generated and run the same query with travel dates three months later. Look at what changes.

If the output is essentially identical — same places, same structure, no meaningful differences — the tool is not using your dates as an input. It's using them as a label. The itinerary it's generating is valid for any visitor to that destination at any time of year.

A tool that actually integrates temporal data will show different live events, different seasonal considerations, potentially different recommendations for what's at its best during that window versus three months earlier or later. What most trip planners are still missing explains why so few tools pass this test.

This test takes two minutes and immediately reveals whether the "personalization" is real or cosmetic.

Test 2: Give It a Preference It Has to Work With

Most trip planners accept preference input. Tell the tool something specific: you want to avoid tourist-facing restaurants, or you're interested in contemporary architecture rather than historical sites, or you want to prioritize outdoor activities over museums.

Then check whether the output is actually different from what the tool would give anyone visiting that destination. Look at the specific places recommended: are they the most popular options filtered for your stated preference, or are they genuinely curated for someone with that interest?

The failure mode here is what might be called preference-washing: the tool acknowledges your preference, selects from the most-reviewed places in that category, and presents the result as personalized. Why AI travel planners feel generic covers the structural reason this happens — it's a data problem, not an interface problem. The Louvre is still in your Paris itinerary because it's the most-reviewed art attraction, even though you asked for contemporary art, which is a different thing.

Test 3: Ask About Something Time-Specific

Ask the tool what events or festivals are happening during your travel dates. Ask whether there's anything time-specific you should know about or incorporate into your plans.

A tool that has live event integration will surface this information. A tool that doesn't will either answer generically (describing recurring seasonal events), acknowledge it doesn't have that information, or — worse — hallucinate an event that doesn't exist.

This test matters because time-specific events are often the most memorable parts of a trip. A tool that can surface these things before you book is genuinely more valuable than one that doesn't, even if it looks similar in a demo.

Test 4: Look at the Explanation, Not Just the List

Ask the tool to explain why it's recommending specific places. Good personalization is explicable: "I recommended this market on Saturday morning because you mentioned interest in local food and this market runs exclusively on weekends." Generic aggregation is also explicable, but differently: "This restaurant has 4.8 stars and 3,000 reviews."

Both answers can produce fine recommendations. But the first type scales to your specific situation; the second type doesn't. If all the explanations are about review scores and popularity, you're getting an aggregated popularity list, which you could get from any review site.

What You're Looking For

The trip planning tools worth using share a specific quality: they treat your travel as a specific event rather than a generic instance of "visiting a city." They know your dates matter. They know your interests should change what you're shown, not just filter which popular places appear. They treat time-specific experiences as relevant information, not edge cases.

The tools that look impressive in a demo but disappoint in use have optimized for the demo: instant output, polished formatting, plausible-sounding recommendations. They haven't optimized for what matters — that the plan you get is specifically suited to you, at that particular time, in a way that makes your actual trip better.

The four tests above separate those two categories reliably.