Key takeaways
- A single national prompt list almost always misreads local AI visibility. Competitors, terminology, and even which content types get cited change city by city.
- ChatGPT runs a live web search for roughly 31% of prompts, and 59% of those searches carry local intent. If your prompt list skips "near me" and city-qualified phrasing, you're missing the majority of what's actually happening.
- Branded local prompts ("[Brand] + [city]") and organic local prompts ("best [service] in [city]") behave completely differently in ChatGPT. Organic prompts cite 10+ domains over half the time; branded prompts narrow to a handful of sources. Track them as separate buckets.
- Google's AI local packs show roughly 68% fewer unique businesses than classic 3-packs, so losing a spot there costs more visibility per query than losing a 3-pack ranking.
- Build prompts from real customer language (support tickets, call transcripts, lead-form fields, review text), not from guesswork, and refresh the list every quarter because model behavior shifts fast.
Why one prompt list for the whole country doesn't work anymore
I'll start with the thing everyone gets wrong first: treating "AI visibility" like classic rank tracking, where you pick 50 keywords and check positions once a week. That mental model made sense for Google's ten blue links. It falls apart for AI answers, because the answer itself changes shape depending on where the question is asked from.
Aleyda Solis makes this point bluntly in her prompt library framework: a prompt that's representative in one city or country can be actively misleading in another, because competitors, terminology, trust signals, and even buying norms shift by market. A plumber in Austin and a plumber in Buffalo are not competing against the same set of businesses when someone asks ChatGPT "who's a reliable plumber near me," and the AI's answer draws on completely different local signals in each case.
That's the whole premise of city-level prompt testing. You're not building one list and running it everywhere. You're building a structure that gets cloned and adapted per market, so you can actually see where visibility holds up and where it quietly disappears.
What actually happens when someone asks an AI a local question
Before building the list, it helps to know what you're measuring against. A few data points change how you should write prompts:
ChatGPT triggers a real-time web search for about 31% of all prompts, and of those, 59% are local-intent, according to Search Engine Land's analysis covered by Local Falcon. That's not a small slice. Local intent is the majority of what ChatGPT's search layer is actually doing behind the scenes.
Those local searches also tend to be longer. At least 75% of ChatGPT prompts that trigger a search are five words or more, about 60% longer than the average Google query. People don't type "plumber austin" into ChatGPT. They type "who's a reliable emergency plumber near downtown Austin that works weekends." Your prompt list needs to reflect that conversational length, not keyword-style fragments.
Promptwatch's own data on query fanouts shows something else worth knowing: the character length of the sub-queries ChatGPT generates behind the scenes has been shrinking, from around 117 characters in December down to roughly 53 characters by April 2026. ChatGPT's internal searches increasingly read like short keyword queries even when the user's original prompt was conversational. That's a useful detail if you're trying to reverse-engineer what content ChatGPT is actually fetching, versus what the user typed.
Branded vs. organic local prompts behave completely differently
This is the part most local prompt lists get wrong: they don't separate branded and organic prompts, and then wonder why the visibility numbers look noisy.
According to Promptwatch's citation data on unique domains per response, organic (non-branded) prompts like "best dentist in Denver" cite 10 or more domains in 52.5% of ChatGPT responses, and at least 8 domains 71.8% of the time. These are crowded answers. You're one voice among many, and the AI is actively sampling a wide set of sources to build its answer.
Branded prompts, like "[Your Dental Practice] Denver reviews," behave the opposite way. Only 21.3% of branded responses cite 10+ domains, and 40.7% cite five or fewer. The field narrows sharply once the brand name is in the prompt. That also means the content types that matter shift: for branded prompts, reviews make up 5.6% of citations (roughly 3x their average share) and social posts jump to about 3%, compared to generic category prompts where product and landing pages dominate.
Practically, that means your local prompt list needs at least two lanes per city, not one:
- Organic/discovery prompts: "best [service] in [city]", "[service] near [neighborhood]", "who should I hire for [problem] in [city]"
- Branded prompts: "[Brand] [city] reviews", "is [Brand] good in [city]", "[Brand] vs [local competitor]"
Running both and comparing results tells you two different things: whether you show up during discovery, and whether your reputation holds up once someone already knows your name.
The content types AI actually cites for local queries
If you're optimizing content to go with your prompt list, know what gets cited. Promptwatch's content-type breakdown for ChatGPT shows product and landing pages account for about 39% of all citations combined, with listicles at 13.6% and comparisons at 5.9%. For commercial and comparison-style prompts specifically, the split tightens further: product pages (26%), landing pages (20.6%), listicles (15%), and comparisons (7.9%) make up roughly 70% of citations. Reviews sit surprisingly low at 1.7% overall, but spike for branded prompts.
On the Google side, a city-specific wrinkle matters a lot: Whitespark found that in Google's local AI Overviews, 60% of citations point to third-party sites (Yelp, Reddit, Thumbtack) rather than the business's own website. That changes the optimization target. Owning your city landing page helps, but it's not sufficient on its own, you also need presence and consistency on the third-party sites AI is actually pulling from.
A framework for building the list, city by city
Here's the structure I'd actually use, adapted from Profound's step-by-step approach and Peec AI's journey-stage framing, applied specifically at the city level.
Step 1: Pick the cities you actually serve, not an arbitrary top-10 list
Start with real service areas, not aspirational ones. If you have five locations, build five prompt sets, not one national set with city names swapped in. A controlled franchise test covering five locations over two months found that local markets varied meaningfully by competitive density, service mix, and even what counted as a conversion signal, enough that a single shared prompt set under-represented the real variation between cities.
Step 2: Give each prompt a distinct customer need, not a template swap
A common mistake (flagged directly in a recent r/aeotips thread on choosing local prompts in Peec AI) is generating city variants by just copy-pasting the same five prompts with a new city name dropped in. That produces near-duplicate prompts that test nothing new. Instead, vary the actual need: urgency ("emergency"), budget tier ("affordable"), specific sub-service, and comparison framing ("vs [specific competitor]") should each get their own prompt per city, but only where that variation is realistic for that market.
Step 3: Source the language from real customers, not your own assumptions
Mine support tickets, sales call transcripts, the "how did you hear about us" field on lead forms (ask for the literal phrase they used, not just the channel), reviews, and community posts. Aleyda Solis's framework calls this out as one of the most common failure points: teams write prompts the way they think customers talk, not the way customers actually talk.
Step 4: Split by journey stage
Awareness-stage prompts rarely mention a brand or even a service category directly. Peec AI's own write-up gives a good example: for an EV brand, a prompt like "will electric vehicles hold their value?" surfaces the brand as "the exception" at the exact moment a buyer has a doubt, well before they've typed "Tesla." For local businesses, the equivalent is pain-point prompts ("why does my basement flood every spring in [city]") that never mention "basement waterproofing company" at all, but still represent a real moment where AI recommendations get made.
Step 5: Keep the list smaller than you want it to be
A tight, well-structured list beats a bloated one. If two prompts in the same city would get materially the same answer, you don't need both. This matters doubly at the city level, because multiplying a 50-prompt national list across 20 cities gives you 1,000 prompts to track, run, and maintain, most of which add noise rather than signal.
Step 6: Run it consistently and don't overreact to one snapshot
AI answers vary by session, time of day, model version, and personalization. One bad run isn't a trend. Run each city's prompt set on a fixed schedule and look for patterns across multiple runs before concluding you've lost visibility somewhere.
Comparing the tools built for this
Most generic rank trackers weren't built with city-level AI prompt testing in mind. A handful of platforms specifically support geographic segmentation of prompt sets.
| Tool | Local/geo prompt support | Starting price | Notable limitation |
|---|---|---|---|
| Peec AI | Multi-country projects, smart prompt suggestions | $95/mo (Starter, 50 prompts) | Country-level, not always granular to individual cities without separate projects |
| Promptwatch | State/city-level tracking on Professional plan, custom personas by location | $95/mo (Essential) | More built for execution (content + CMS publishing) than pure local-pack monitoring |
| Otterly.AI | Location-based monitoring on entry tier | $29/mo | Only 15 prompts on the cheapest plan, limits how many cities you can cover |
| Birdeye Search AI | Location-level tracking built for multi-location brands | Custom | Geared toward franchise/multi-location reputation, less flexible for pure prompt experimentation |
| Rank Prompt | Location-based prompt variation, multilingual support | Custom/tiered | Newer platform, smaller public track record |
Rank Prompt
If your business operates in two or three cities and you want prompt tracking paired with the ability to actually publish location-specific content and see which pages AI crawlers are reading, that's where Promptwatch's crawler logs and content agents are worth a look, since most of the pure monitoring tools above stop at telling you whether you were mentioned.
What's shifting the ground under local AI visibility right now
A few structural changes are worth tracking into 2026 regardless of which tool you use.
Google's AI local packs are expanding and they're brutal for visibility. Sterling Sky's research, cited by The Stacc, found AI local packs show roughly 68% fewer unique businesses than a traditional 3-pack, typically surfacing just one or two businesses instead of three, and dropping the call button entirely. Across 322 tracked markets, 88% lost some visibility when AI packs replaced standard local-pack results.
Review recency now beats review volume. Since the March 2026 core update, a business with 20 reviews in the last 90 days reportedly outranks one with 200 reviews from several years ago. If your prompt list includes review-sensitive branded prompts, pair the tracking with an actual review-generation cadence, not just monitoring.
And the trigger rates vary wildly by vertical. Restaurants went from a 10% AI Overview trigger rate to 78% in under a year; e-commerce queries barely moved (2% to 4%). If you're building a prompt list across multiple business categories, don't assume AI visibility matters equally everywhere, measure it first.
If you want to go deeper on how AI search engines treat content types and citations more broadly (not just local), Promptwatch's research hub at promptwatch.com/data tracks this monthly across ChatGPT, Gemini, Perplexity, and Google AI Overviews, and it's worth checking before you finalize what content format to pair with each city's prompt set. For a broader look at the GEO tooling space beyond what's covered here, the directory at bestgeosoftware.com is a reasonable starting point for comparing platforms side by side.