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AI-Powered Travel Planning: Custom Itineraries for New England Adventures
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AI-Powered Travel Planning: Custom Itineraries for New England Adventures

Feb 16, 2026

New England is a funny place to plan a trip. The distances look short on a map, and then you remember Route 16 exists, that the Kancamagus Highway is a parking lot on peak foliage weekends, and that “quick stop at the beach” might mean a 30-minute hunt for parking in August. This is where AI travel planning gets interesting: not because it writes a cute schedule, but because it can juggle constraints, preferences, seasons, and logistics… if we set it up right.

At NH AI Meetup, we end up talking about this a lot: LLMs are great at ideas and narrative, and mediocre at “what’s actually open on Tuesday” unless you connect them to real data. In this post, we’ll cover how to get genuinely useful AI-powered itineraries for New England adventures, plus a small tutorial for folks who want to build a simple planner.

What AI is good at (and what it still messes up)

A large language model can:

  • Generate options fast: a dozen rainy-day activities in Portsmouth, or three different White Mountains weekend themes (hikes, breweries, kid-friendly).
  • Personalize: gluten-free + toddler naps + “I hate crowds” is a normal ask, and AI can handle it.
  • Optimize loosely: it can cluster activities by area, suggest morning vs afternoon pacing, and keep a running “packing list” style plan.

But it tends to:

  • Hallucinate specifics: it might invent a shuttle schedule, a restaurant that closed in 2022, or a trail condition report that doesn’t exist.
  • Underestimate travel time: a 2-hour drive becomes 1:15 because the model thinks in clean numbers.
  • Ignore New England reality: weather shifts, seasonal closures, mud season in VT, limited weekday hours in small towns.

So the trick is: use the model for reasoning and structure, and use tools/data for facts.

A practical workflow: “LLM as planner, APIs as truth”

If you’re just using a chat interface, you can still mimic this workflow.

  1. Start with constraints and vibe

Give it your non-negotiables first. Not the destination list. Things like:

  • Dates and season (“Oct 12–14, peak foliage maybe, two adults”)
  • Starting point (“Leaving from Manchester, NH”)
  • Driving tolerance (“Max 2.5 hours per day”)
  • Activity intensity (“One big hike, otherwise chill”)
  • Budget (“Mid-range, not fancy”)
  • Food needs, accessibility, dog-friendly, etc.
  1. Ask for an itinerary skeleton, not final details

Skeleton means: towns/areas, time blocks, and backup options. Then you verify the specifics.

  1. Verify facts in a second pass

Have the AI produce a checklist of things to confirm: opening hours, reservations, trailhead parking, ferry schedules, and weather. You can do this manually, or programmatically if you’re building an app.

  1. Regenerate with verified facts

Once you confirm the key points, ask the model to rewrite the itinerary using only the confirmed items. This simple step reduces the “sounds right but isn’t” problem.

Prompt template that works weirdly well

Here’s a prompt you can copy/paste. It’s not magic, it just forces clarity.

Prompt:

  • You are a travel planner for New England. Create a 3-day itinerary.
  • Start: {starting_city}
  • Dates/season: {dates}
  • Travelers: {who}, with {constraints}
  • Interests: {interests}
  • Driving limit: {max_driving_per_day}
  • Lodging style: {hotel/inn/cabin}, budget: {budget}

Rules:

  1. Provide a morning/afternoon/evening plan for each day.
  2. Keep driving realistic; include estimated drive times.
  3. Include one “Plan B” indoor option each day in case of rain.
  4. Flag anything that likely needs reservations or has seasonal hours.
  5. Don’t invent exact hours or pricing; instead list what must be verified.

Output format:

  • Day-by-day schedule
  • Reservation/verification checklist
  • Packing suggestions based on season

If you do only one thing, do this: tell it not to invent hours/prices. You’ll get fewer confident lies.

Example: A long weekend built by AI (and refined)

Let’s say you’re leaving from Concord, NH in early October, want foliage, moderate hiking, good coffee, and not too much driving.

Day 1: North Conway / Kancamagus area (base night)

  • Morning: Drive to North Conway (about 1.5–2 hrs depending on traffic), stop for coffee and a quick walk around town.
  • Afternoon: Scenic drive on the Kancamagus with short hikes (think viewpoints + something like a 1–3 mile loop). Keep it flexible because foliage traffic is a thing.
  • Evening: Casual dinner and an early night.
  • Plan B (rain): Conway Scenic Railroad, a bookstore crawl, or just commit to cozy: café + local shops.
  • Needs verification: trailhead parking rules, “best time to leave” to avoid gridlock, restaurant waits.

Day 2: One “big” hike + a recovery evening

  • Morning: Pick one moderate classic (you’ll choose based on your ability and conditions) and go early. New England rule: early is everything.
  • Afternoon: Late lunch, then something low-effort: covered bridge hunt, short waterfall walk, or a brewery.
  • Evening: Dinner somewhere you can reserve, or commit to takeout and a chill night.
  • Plan B: A museum/indoor activity, plus a drive through a notch when the clouds break.
  • Needs verification: trail conditions, weather window, whether your hike is still safe with wet ledges.

Day 3: Portsmouth detour on the way home

  • Morning: Drive south toward the Seacoast (2+ hrs from the Whites). Coffee, walk the waterfront.
  • Afternoon: Quick lunch, maybe a short beach walk if it’s not windy-cold.
  • Evening: Head home.
  • Plan B: Strawbery Banke-style history stops, cozy seafood place, or a couple shops and call it.
  • Needs verification: parking situation, seasonal hours.

This is a solid AI skeleton. The human step is choosing the actual hike and confirming what’s open. And honestly, that’s fine.

If you want to build a simple AI itinerary tool (mini tutorial)

A surprisingly effective architecture for a “New England itinerary bot” is:

Diagram of an AI travel planner workflow: user constraints to LLM parser, retrieval from local places data, LLM scheduling using only retrieved items, optional maps/weather verification, and a final regenerated itinerary with a checklist.

  1. A constraints parser (LLM)
  2. A retrieval layer (local data) for “things to do”
  3. A planning step (LLM) that turns retrieved items into a schedule
  4. Optional verification with live tools (maps, weather)

1) Build a local “places” dataset

Start small: a JSON or CSV with attractions, towns, categories, seasonality notes, approximate time needed, and tags like rainy_day, kid_friendly, outdoors, accessible.

Example fields:

  • name, town, state
  • category (hike, museum, food, scenic drive)
  • seasonality (all_year, summer_only, shoulder_season)
  • time_needed_hours
  • notes (parking tricky on weekends, reservations recommended)

This is your anti-hallucination anchor.

2) Add retrieval (RAG-lite)

Embed your place descriptions (or just keyword match at first) and retrieve top candidates based on the user’s interests and base location. You don’t need a fancy vector database to start; a simple “filter by distance + tags” gets you far.

Pseudo-steps:

  • Filter to states the user is willing to visit (NH/ME/VT/MA/RI/CT).
  • Filter by seasonality.
  • Score by matching interests/tags.
  • Keep 20–40 candidates.

3) Ask the model to plan using ONLY retrieved items

This is the key instruction:

  • “Use only the following list of places. If something is missing, ask a question instead of inventing it.”

Then include the retrieved list and ask for a day-by-day plan.

4) Add a “sanity check” pass

One more LLM call can catch issues:

  • total driving too high
  • too many big activities stacked
  • missing meal breaks
  • not enough buffer for fall traffic

Prompt it like a picky friend: “Find problems in this plan and propose fixes.”

Trends we’re seeing (and why it matters for New England)

  • Tool-using agents are getting practical: models that can call a maps API, check weather, then revise the itinerary are finally moving from demo to usable. Still brittle, but getting there.
  • Personalization is the new baseline: people want “my kind of weekend,” not “top 10 things.” AI is good at that, as long as it’s grounded.
  • Local data wins: the best itineraries come from local knowledge—trail closures, shoulder-season hours, festival weekends. Community-curated datasets are underrated, and honestly a fun meetup project.

A few guardrails (because travel has real stakes)

  • Don’t trust AI for safety-critical outdoors decisions: weather, avalanche risk, river crossings, hunting seasons, etc. Use official sources.
  • Be careful with privacy: if you’re building a tool, don’t store precise location histories unless you really need them.
  • Encourage users to verify: an itinerary that includes a “verification checklist” is better than one that pretends certainty.

Try this at home, then bring it to the meetup

If you want a low-effort experiment: take your next weekend idea and run the prompt template above, then spend 10 minutes verifying the top 3 items. You’ll feel the difference immediately. The AI becomes less of a “travel blogger generator” and more of a competent assistant.

And if you’re the type who wants to build it, bring your rough prototype to a NH AI Meetup night. A shared New England places dataset, a clean planning prompt, and a couple tool calls (maps + weather) gets you a planner you’d actually use. Not perfect. But pretty darn good, and that’s the whole game.

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