Intro
The framework for building a prompt set is generic. Hotels are not.
A resort marketing team, a boutique group’s revenue manager, and a city-hotel commercial director all face the same AI visibility problem — but the prompts that decide their fate are specific to hospitality, specific to their segment, and often specific to the season. Generic prompt templates miss the queries that actually drive room nights.
This page is the hotel-specific companion to how to build a prompt set. It shows the four intent buckets with concrete example prompts, the five places to mine seed prompts inside the property, and a one-week sprint plan to lock the first 40-prompt library.
Get your hotel prompt library scored
Why hotels need a hotel-specific prompt library
A generic “best hotels in Europe” prompt set will not move a single booking for a property whose guests are searching “best adults-only resort in [destination] for a honeymoon” or “[boutique hotel in destination] family-friendly”.
Hotels live in the long tail of intent. The buying journey crosses Discovery (“where should we go”), Comparison (“which of these two”), Trust (“is it actually worth it”), and Conversion (“book direct or via Booking”). Each bucket needs its own prompts — and the prompts that matter for a five-star city hotel are not the prompts that matter for a beach resort.
A hotel-specific library does three things a generic set cannot:
- Surfaces the queries where OTAs intercept the booking
- Reveals which competitors AI engines pair the brand with
- Exposes the trust questions guests ask AI before clicking “book”
The library is the foundation of the hospitality scorecard. Without it, every downstream measurement is noise.
Four intent buckets with example prompts
Each example uses generic placeholders — [destination], [hotel], [brand A], [X price tier] — to be adapted to the property’s market and segment.
Discovery
Broad queries that surface a shortlist before the guest knows the brand.
- best luxury resort in [destination] for families
- where to stay in [destination] for a honeymoon
- top adults-only hotels in [destination]
- best boutique hotel in [destination] for a long weekend
- five-star wellness retreats near [destination]
- where to stay in [destination] with kids under 10
Comparison
Head-to-head and shortlist-narrowing queries.
- [brand A] vs [brand B] which is better for couples
- five-star resorts in [destination] under [X price tier]
- [hotel] vs [competitor hotel] for a family of four
- best alternative to [well-known hotel] in [destination]
- [hotel] or [competitor hotel] for a wedding venue
- compare [hotel] and [competitor hotel] spa facilities
Trust
Review-driven, reputation-driven, accuracy-driven queries.
- is [hotel] worth it
- real reviews of [hotel]
- is [hotel] family-friendly
- is [hotel] really five stars
- [hotel] honest opinions from recent guests
- what are the downsides of staying at [hotel]
Conversion
Branded queries where the answer drives or breaks the booking.
- book directly [hotel] vs Booking.com
- [hotel] cancellation policy
- best rate guarantee [hotel]
- [hotel] direct booking benefits
- [hotel] official website
- [hotel] phone number to book
A first library covers all four buckets with roughly 10 prompts each. Skewing too far toward Conversion means missing where guests first hear of the brand; skewing too far toward Discovery means missing where OTAs steal the booking.
Five sources for seed prompts
Seed prompts are mined, not imagined. The five places to find them, in order of value:
- PMS and booking data — extract the search terms guests reported, the room types they considered, the package names they asked about. This is the only first-party intent signal the hotel owns.
- GA4 and Search Console — pull branded and non-branded queries that already drive sessions. Anything Google considers a query worth ranking, AI engines consider a query worth answering.
- Customer service logs — read three months of emails, chat transcripts, and call notes. The questions guests ask before booking are the trust prompts AI engines will face.
- Sales calls and concierge requests — the wedding planner, MICE sales, and front-desk staff hear the comparison and trust questions in plain language. Capture them verbatim.
- OTA review responses — the questions guests ask in reviews, and the objections the team answers, are a direct map of trust-bucket prompts.
A team that completes these five sources usually arrives at 60-80 raw seed prompts. The next step compresses that to a locked 40.
How to expand a seed prompt into a cluster
A single seed rarely captures the full intent. Expansion uses the AI engines themselves.
Take a seed: “best luxury resort in [destination] for families”. Then:
- Type it into ChatGPT and capture the related question suggestions
- Run it through Perplexity and read the “Related” panel
- Search it on Google and harvest the “People also ask” entries
- Add a variant for each segment the hotel actively targets (couples, families, business, wellness)
A good seed expands into four to six clustered prompts. Across 40 seeds, that produces a library of 160-240 candidates — far too many. Clustering and de-duplication then bring it back to the locked 40.
The principle: capture every variant the AI engines treat as a distinct query, then collapse the ones that produce identical answers.
The one-week sprint plan
A first library is built in five working days, not five weeks. Any longer and the team loses momentum before the baseline measurement is even possible.
Day 1 — Mine seed prompts. Pull data from PMS, GA4, customer service, sales, and OTA reviews. Target: 60-80 raw seeds.
Day 2 — Sort into intent buckets. Assign each seed to Discovery, Comparison, Trust, or Conversion. Flag prompts that don’t fit — they are usually too generic or too internal.
Day 3 — Expand and cluster. Run each seed through ChatGPT, Perplexity, and Google to harvest variants. Group near-duplicates.
Day 4 — Compress to 40. Cut the library to 10 prompts per bucket. Prioritise prompts where the commercial stakes are highest (Conversion bucket gets the strictest curation).
Day 5 — Lock and document. Freeze the prompt list, document the rationale for each prompt, and hand it to whoever will run the baseline. From this point, the library does not change for at least a quarter.
The locking step is the one most often skipped — and the one that makes every later comparison meaningful.
How this fits into Capston Core
A hotel prompt library is the input the rest of Capston Core depends on. It feeds the baseline measurement, it structures the hospitality scorecard, and it sits inside the broader Capston Core methodology as the first deliverable of stage one. The generic framework for building any prompt set is documented in how to build a prompt set — this page is its hospitality-specific application.
→ Back to Capston Core
FAQ
Can a hotel use a competitor’s prompt library?
No. Two hotels in the same destination serve different segments, price tiers, and travel occasions. A library built for one will miss half the queries that decide bookings for the other.
How many prompts should a single property start with?
Forty is the standard first library — ten per intent bucket. A multi-property group adds 15-20 portfolio-level prompts on top.
How long does the library stay locked?
At least one quarter. Locking is what makes quarter-on-quarter comparison valid. Mid-cycle additions invalidate the trend.
Who in the hotel team should own the library?
Commercial direction owns the library. Marketing, revenue management, and front-of-house contribute seeds; the commercial director signs off on the locked set.
Final CTA block
Build your first hotel prompt library in one week.
Get a free audit
Read the framework