Key takeaways
- "Local prompts" means two different things. This guide covers the one that matters for visibility: the questions customers type into ChatGPT, Gemini, Perplexity and Google AI Mode when they want a business nearby.
- Real local prompts carry a situation, a constraint and a place, not just "service + city." Your tracking set should too.
- AI tools turn one prompt into several short web searches. Promptwatch Data puts the recent average at roughly 1 to 2 searches per response, so your pages have to match short keyword-style queries as well as long questions.
- ChatGPT started using site: searches at scale on August 8, 2026, so crawlable, indexed location and service pages matter more than they did a few months ago.
- Build 30 to 50 prompts across five intent types, track them by location, and review the pages that get cited, not just whether you were mentioned.
Two meanings of "local prompts" (and why mixing them up hurts)
Search for this topic and most of what you find is about prompts a business owner writes to an AI tool: a social post for the cafe, a sign for the daily special, a reply to a review. Guides like the Founderz piece on AI prompts for local commerce and Tracy Sheen's small business prompting guide are good examples. They're useful. They're also a different job.
This guide is about the other direction. A customer opens ChatGPT or Gemini and types something like "where can I get my dog groomed this weekend, no appointment, near Rose Park." Whether your business shows up in that answer depends on how well you've understood that sentence. Writing prompts that mirror it is how you test, track and improve your odds.
So there are two lists:
| Type | Who writes it | Goal | Example |
|---|---|---|---|
| Content prompts | You, to an AI tool | Produce posts, pages, emails | "You are a social media writer for local cafes. Write three Facebook posts about our winter menu." |
| Customer prompts | Your customers, to an AI assistant | Find a nearby business | "Best sourdough bakery open before 7am in Byron Bay" |
You need the second list to decide what the first list should produce. Keep them separate in your head and in your spreadsheet.
How customers actually phrase local questions
I want to be upfront about something: there isn't a clean public dataset of how people phrase "near me" prompts to AI assistants. Promptwatch Data doesn't have one, and I won't invent numbers. What you can do is reason from how people talk and then check your assumptions against real tracking data.
The pattern that shows up again and again is that AI prompts are longer and more specific than classic search queries. People talk to an assistant the way they'd talk to a friend. They add context that a search box never invited.
The five ingredients of a realistic local prompt
Most customer prompts combine some of these:
- The need ("emergency plumber," "vegan birthday cake," "physio for a runner's knee")
- The place, at varying precision (a city, a suburb, a landmark, "near my office," "on the way to the airport")
- A constraint (open Sunday, under $150, wheelchair accessible, speaks Spanish, takes walk-ins)
- A situation ("my kid has a tournament Saturday," "I just moved here")
- A decision request ("which one should I pick," "compare the top three," "is X worth it")
A keyword tool will give you "plumber Denver." A customer gives the assistant "My water heater is leaking in a Denver rental, who can come today and what should I expect to pay?" Both are local. Only one resembles a real prompt.
Five intent types to cover
| Intent | What the customer wants | Sample prompt |
|---|---|---|
| Discovery | A shortlist | "Best family dentist in Leeds that takes new NHS patients" |
| Urgent | Someone available now | "Locksmith open right now near Tempe, how much?" |
| Comparison | A decision between options | "Is Pilates or yoga better for a bad back, and which studio in Austin is good for beginners?" |
| Trust check | Validation of a name they have | "Is Harbor Street Auto Repair legit? Reviews?" |
| Planning | A multi-step answer | "Plan a dog-friendly Saturday in Asheville with lunch and a bakery" |
If your tracking set is 40 variations of discovery prompts, you'll miss most of the situations where customers actually decide.
Build your local prompt set step by step
Start from sources that hold real language
Don't brainstorm in a vacuum. Pull phrasing from places customers already talk:
- Your own reviews. The U.S. Chamber's small business prompt list suggests feeding recent reviews to an AI and asking for top issues. Use the same trick for language: ask it to list the exact phrases customers use to describe why they came.
- Phone and chat logs, plus the questions your front desk answers daily.
- Google Search Console queries for your location pages (Google Search Console is free).
- People Also Ask and autocomplete. Tools like AlsoAsked and AnswerThePublic surface real question phrasing.
- Reddit and Facebook community threads where locals ask for recommendations.
Use an AI assistant to expand, then prune
Once you have 10 to 15 real examples, ask a general assistant to generate variations. This is where the content-prompt skill from those small business guides comes in. A reasonable starting prompt:
You are a customer researcher. I run a [business type] in [suburb, city].
Here are 10 real questions customers have asked us: [paste].
Write 40 new questions a customer might ask an AI assistant when looking
for a business like mine. Vary the situation, the constraint and how
precisely they name the location. Include 8 urgent requests, 8 comparison
questions and 8 trust-check questions. Return a table with the question and
the intent type.
Then prune. Delete anything no real person would say. Synthetic prompts tend to be tidier than real ones, so rough up a few by hand: add typos, drop the grammar, say "nr" for near.
Vary the location precision
Location is where most local sets fall apart. Write the same need at several levels:
- City: "best pediatric dentist in Portland"
- Neighborhood: "pediatric dentist Alberta Arts District"
- Relative: "pediatric dentist close to Providence Park"
- Implied: "pediatric dentist near me" (the assistant infers location from the device or account)
Implied-location prompts are the hardest to track because the answer depends on where the person is standing. Tracking platforms that let you set a country, state or city handle this better than checking by hand from your own office.
What happens after the customer hits send
This part changes how you should write your pages.
When an assistant needs live information, it doesn't search your customer's full sentence. It breaks the request into smaller web searches, called query fan-outs. Promptwatch's ChatGPT query fanout data describes one prompt triggering several searches, but the measured averages are lower than that framing suggests: about 2.15 fan-outs per response in early December, about 1.84 by early March, and exactly 1.0 when the data resumed in April. Fan-out query length also shrank, from about 117 characters in December to about 53 in April, roughly six to eight words.
Translate that into local terms. The customer says "my water heater is leaking in a Denver rental, who can come today." The assistant may search something like "emergency water heater repair Denver." Short, keyword-like, entity first. Your page needs to match that shorter query, even though the prompt was a paragraph.
The same report recommends writing queries and headings like keyword searches rather than conversational questions, putting the entity and category first, and covering the sub-questions a fan-out splits into: comparisons, pricing, alternatives and how-tos. For a local business, that becomes:
- Headings like "Emergency water heater repair in Denver" instead of "What should I do when my water heater breaks?"
- A pricing section with real ranges
- Hours, service area and response time stated plainly in text
- A short comparison or "how to choose" section
This behavior changes. Promptwatch Data recorded ChatGPT starting to use the site: operator at scale on August 8, 2026, jumping from about 0.4% to about 17% of fan-out queries overnight, with searches per response nearly doubling. That suggests ChatGPT may search a specific domain, such as your site or a directory listing, directly. If your location pages are blocked, thin or not indexed, there's nothing for that search to find. Treat that as an implication of the data, not a proven local-specific effect.
Turn your prompt set into a test
A list of prompts is only useful if you run it and record what comes back. For each prompt, note:
- Whether your business is mentioned
- Where it appears in the answer
- Which sources the assistant cites (your site, a directory, Reddit, a review site)
- Which competitors appear and why
- Whether the details are right (hours, address, services)
The cited sources are the most valuable column. If the answers keep citing a directory instead of your own page, you have a listing and consistency problem. If they cite a competitor's service page that answers a question yours doesn't, you have a content gap.
Manual versus tracked
You can do a first pass by hand. Open a fresh session in ChatGPT, Gemini and Perplexity, paste 20 prompts, screenshot the results. It's tedious but it teaches you quickly. Expect noise: answers vary between runs and between locations.
Past the first pass, tracking software earns its keep. Here is how the options in our catalog break down for local work:
| Tool | What it does | Local relevance | Best for |
|---|---|---|---|
| Promptwatch | Tracks prompts across AI models with country and (on higher plans) state/city tracking, citation data, crawler logs and content actions | Strong: location-level tracking plus fixing content gaps | Businesses that want to act on the results, not just watch them |
| Otterly.AI | Prompt-level monitoring of brand mentions | Basic monitoring | Light tracking on a small budget |
| Peec AI | AI search visibility tracking | Monitoring focus | Marketing teams wanting dashboards |
| BrightLocal | Local SEO platform for listings, reviews and local rankings | Strong on classic local SEO | Multi-location citation and review management |
| Yext | Multi-location listings and brand visibility | Strong on structured data distribution | Chains with many locations |
| Local Falcon | Geo-grid rank tracking | Map-pack position by area | Seeing how rankings change block by block |
Promptwatch is the one I'd point most local operators to when they want to go beyond "was I mentioned." It tracks real AI interfaces rather than only APIs, supports country tracking on every paid plan and state and city tracking from the Business plan, and shows which of your pages get cited and which AI crawlers are hitting your site. Prompt volumes and difficulty scores help you decide which of your 40 prompts deserve attention first. Check promptwatch.com/pricing for current plans.
For classic local signals, BrightLocal and Local Falcon cover listings, reviews and map rankings, which still feed what AI assistants know about you.
Otterly.AI
If you want to compare more options, the directory of AI rank tracking tools and the GEO software directory list the wider field.
Fix the pages that keep you out of answers
Tracking tells you where you're missing. These are the fixes that tend to matter for local businesses.
One page per service per location, with real substance
A template page with the city name swapped is not what an assistant wants to cite. Housecall Pro's prompt guide suggests asking an AI for a service page outline targeting "[service] in [city]." That's a fine way to draft a structure. But fill it with things only you know: neighborhoods you serve, typical job times, price ranges, photos from real jobs, the questions customers ask at the door.
State the boring facts in plain text
Hours, address, phone, service area, accepted payment, languages spoken, accessibility. Assistants fail when these are buried in images or scripts. Put them in text near the top of the page.
Make sure the crawlers can read you
Check robots.txt for AI bots, confirm key pages are in your sitemap, and make sure the content renders without JavaScript if possible. Server logs or a tool that shows AI crawler visits will tell you whether GPTBot, OAI-SearchBot and others are reaching your pages or hitting errors.
Keep your listings consistent
Assistants triangulate. If your Google Business Profile says one thing, Yelp says another and your website says a third, the answer may be wrong or you may be skipped. Platforms like Moz Local, Yext and Whitespark exist to keep this aligned.
Don't rely on Reddit or social to carry you
It's tempting to chase community threads. Promptwatch Data shows ChatGPT cited Reddit far less after August 14, 2026 (about 4% of citations to about 0.5%), and social platforms made up just 0.46% of ChatGPT citations from Sep 28 to Oct 4, 2026, versus 12.2% in Google AI Overviews. Which assistant your customers use changes where your effort pays off.
Expect many sources per answer
Almost half of ChatGPT responses cite 10 or more unique domains (Sep 28 to Oct 4, 2026). For a local business, that's an opening: you don't have to be the only source, you have to be one of the sources, and your listings, reviews and own site all count.
Use content prompts to fill the gaps
Now the first meaning of "local prompts" becomes useful again. Once you know which customer questions you're missing, write content prompts that answer them with local specifics. Tracy Sheen's four-part ask (who the AI is, who you are, what you need, what shape the answer takes) works well here:
You are a local SEO copywriter. I run a family-owned HVAC company serving
north Dallas suburbs. I need a service page section answering "how much
does AC repair cost in Plano" in a plain, honest tone. Return 150 words,
one price range table, and a one-line booking call to action. Use only
these facts: [paste real prices, hours, service area].
Give it the facts. The Founderz guide makes the same point: AI produces fast drafts, but a human has to check prices, local details and brand tone before anything is published. An assistant will happily invent a service area or a warranty you don't offer.
If you produce a lot of content, writing tools like Jasper or Frase can speed up drafting, and Promptwatch's Content Agent can plan and publish against the gaps it finds if you'd rather automate the loop. Keep a human review step either way.
A simple monthly routine
- Week 1: Refresh your prompt set. Add three to five new prompts from recent reviews, calls and seasonal needs.
- Week 2: Run or review tracking. Note mentions, positions and cited sources by location.
- Week 3: Fix the top three gaps: one page rewrite, one listing correction, one new section answering a missed question.
- Week 4: Check crawler access and whether last month's changes are being read and cited.
The goal isn't a perfect score. It's a loop you actually keep running.
Common mistakes
- Tracking only "service in city" prompts and calling it done
- Using prompts nobody would say, written in marketing language
- Treating one run of one prompt as a result; answers vary
- Ignoring which sources get cited and fixating on whether you were named
- Publishing AI-drafted pages without checking facts
- Assuming behavior is stable. Platform search behavior can change overnight, as the August 8 site: change showed
Where to start today
Write 20 prompts the way your customers would say them, spread across urgent, comparison and trust-check intents, with at least three levels of location detail. Run them in two or three assistants, record which sources get cited, and fix the single page or listing that shows up most often as the gap. That's one afternoon of work, and it tells you more than another month of guessing.