Prompt Sets for Multiple Countries and Languages

A single prompt set measures one market. Run it everywhere and you learn little about the rest.

Three things change between countries and languages:

• Whether an AI answer appears at all. Google says AI Overviews often do not trigger. Where they do appear varies by location and language, so the same query can show an overview in one country and plain results in another.
• What the engine searches for. OpenAI says ChatGPT search typically rewrites your prompt into one or more targeted queries before sending them to its search providers. Google's Gemini API grounding generates its own queries too. A prompt in Spanish produces Spanish retrieval queries, which pull from different pages.
• Which sources compete. Local review sites, local competitors and local regulators fill the answer. A brand that dominates English answers can be absent in Dutch ones.

It follows that each market needs its own prompt set, its own run conditions and its own line in the report. If you want the background on why brand descriptions shift, see whether AI describes your brand differently by country.

Translation keeps the words. Localization keeps the buyer. You want the second.

Say you sell accounting software in the UK and Germany (a hypothetical). A UK buyer might ask for bookkeeping software for sole traders that handles Making Tax Digital. A German buyer might ask for Buchhaltungssoftware for freelancers with a DATEV export. A translated English prompt would mention neither, and would test a question no German buyer types.

When you localize, change these:

• The problem. Use the local regulation, tax rule, payment method or standard that drives the purchase.
• The competitors. Name the brands buyers compare you with in that market, not your home-market rivals.
• Money and units. Use local currency, price points and measurement units.
• Register and phrasing. Write the way a local buyer writes. Formal versus informal address matters in many languages.
• Intent mix. Keep the same blend of category, comparison and brand-fact questions, so markets stay comparable.

Keep one thing constant across markets: the facts you check. Your price, founding year and product names should be scored the same way everywhere, even if the question wording differs. Have a native speaker who knows the category review every prompt. Machine translation is a draft, never the final set.

Start from your existing method in how to build a prompt set, then adapt it per market.

1. Pick the markets that matter. List countries by revenue or pipeline. Treat one country with two languages (Canada, Switzerland, Belgium) as two markets.
2. Collect local buyer questions. Pull from local sales calls, support tickets, site search and country-level Search Console queries. Write 15 to 30 questions per market, following the sizing logic in how many prompts you need.
3. Write the facts sheet per market. Record local pricing, availability, local entity name and local competitors. This is what you score answers against.
4. Fix the run conditions. Set location and language for each run. Use a logged-out session with memory off. OpenAI says that if memory is enabled, ChatGPT may use saved memories when rewriting a search query, which breaks reproducibility. The ChatGPT prompt tracking method covers the rest of the setup.
5. Run every engine separately. Record for each answer: market, engine, date, whether it searched the web (visible citations), whether your brand was named, the URLs cited, and any wrong fact.
6. Record AI Overview triggers per market. For Google, note how many of the market's queries showed an overview at all. A low trigger rate is a market fact, not a failure.
7. Freeze the set. Version it. Change prompts only on a scheduled review, so a trend reflects the engines and not your edits.

Run through this before every market run. Missing one item usually means the results cannot be compared with last month.

• Prompts written or reviewed by a native speaker who knows the category
• Local competitors, currency and regulations reflected in the wording
• Facts sheet updated for that market (price, plans, availability, legal entity name)
• Location and language set explicitly, not inherited from your own browser
• Logged-out session, memory and custom instructions off
• Same prompt set version as the previous run
• Each engine run separately, never pooled
• Retrieval state recorded per answer (with or without visible citations)
• AI Overview trigger count recorded for Google queries
• Local site version checked: the localized page loads, and its key facts are in the HTML before JavaScript runs
• AI crawlers and the local domain or subfolder allowed in robots.txt and not blocked at the CDN

The last two items are access checks. They are the levers with the strongest evidence: if a local page renders its prices only after JavaScript runs, most AI crawlers other than Googlebot cannot read them.

Good and bad here describe the measurement, not your brand's performance.

A good market report

• One row per market and per engine, with dates and prompt set version
• Mention rate and wrong-fact rate stated separately for answers with and without web search
• AI Overview trigger rate shown next to the citation data, so a market with few overviews is not read as a loss
• Wrong facts quoted, with the source page each engine cited
• Engine-reported numbers (Search Console, Bing Webmaster Tools) shown alongside, clearly labelled as a different instrument

A bad market report

• One global score averaging all countries and engines
• English prompts run from one office, labelled "international"
• A "position" or "rank" in AI answers. Google exposes impressions only for its AI features, so no such number can be checked
• Month-over-month changes after the prompts were rewritten
• No note of which answers searched the web

One engine can behave very differently from the rest. In Otterly's 2026 experiment, Microsoft Copilot produced 2,718 of one test site's 3,934 tracked citations while ChatGPT, Gemini and Google's AI features barely cited it. A blended score would have hidden that. The same logic applies across countries.

Prompt tracking is a sample of questions you chose. Engines see real traffic. Use both, per market.

• Google Search Console has a generative AI performance report with a Countries dimension. As of October 2026 it shows impressions only: no clicks, no CTR, no position.
• Bing Webmaster Tools shows grounding queries in its AI Performance report: the phrases the AI actually used when retrieving cited content. Microsoft notes that grounding query data is a sample.

Compare each market's grounding queries with your prompt set. If local buyers trigger retrieval with phrasing you never test, add it at the next scheduled review. The trade-offs are covered in prompt-based tracking vs engine-reported data. For a quick look at whether Google shows an overview for a query in a given city and language, try the free AI Overview check by city and language.

Monthly per market is a sensible default. It catches model and index changes without burying you in noise.

Rerun a single market straight away when something changes there:

• A local launch, rebrand or price change
• A migration of the local domain, subfolder or CDN
• A change to robots.txt or firewall rules
• A wrong fact found in a previous run, after you fixed the source page

Review the prompts themselves each quarter. Swap in phrasing from grounding queries and sales calls, then bump the version and note the date. Keep the facts sheet live: a stale local price in your own reference turns correct answers into false errors.

Be plain about what you control. You control access, rendering, and the facts on your local pages and on the local sites that name you. You do not control what each model says, and no cadence guarantees a mention.