The representative prompt library: how to measure AI visibility you can actually rely on

What is a prompt library?

A prompt library is a structured collection of prompts — the requests you send to large language models (LLMs) like ChatGPT, Claude or Gemini. The term comes from prompt engineering, the discipline of phrasing inputs to generative AI so the output becomes dependable; the technical basis underneath is natural language processing (NLP).

Two different things share that name in daily practice. The management library collects reusable working prompts for a team: knowledge management in Notion, Google Sheets or a database, one building block of workflow automation — that’s what most German-language definitions mean. This page is about the second kind, the measurement library: a representative prompt set that acts as the input layer of AI visibility tracking and measures whether and how a brand shows up in AI answers. Both kinds share three principles — clean metadata, a taxonomy that holds up, regular maintenance.

Why this matters now

Before the method, four facts that explain why European companies need a system for measuring AI visibility.

1. AI Overviews take most of the clicks away from the top positions. Ahrefs analysed 300,000 keywords in December 2025 and found that the presence of an AI Overview cuts the click-through rate of the page in position 1 by 58% on average. In April 2025 the same figure was 34.5%. Position 2 loses about half its clicks, position 10 close to 20%.

2. Users rarely click the cited sources. Pew Research Center tracked the behaviour of 900 users in March 2025. With an AI Overview present, 8% of users click through to a result page, against 15% without one.

3. A click on a cited link inside an AI Overview happens in 1% of cases. The session ends without any onward click in 26% of cases, against 16% in normal search.

4. Optimising for generative engines works, measurably. An academic study by Princeton, Georgia Tech and IIT Delhi (KDD 2024), benchmarked on 10,000 queries, showed that targeted GEO methods — adding source citations, expert quotes and statistics — raise the visibility of content in generative answers by up to 40%. Classic keyword stuffing performs worse than no optimisation at all.

What those four add up to: click traffic from search is shrinking. Presence in AI answers is becoming a visibility channel of its own, and that visibility can be steered — but only if it’s measured properly. That’s what the prompt library is for.

The problem: non-representative prompt sets

One of the main mistakes in AI search tracking is using a non-representative prompt library to judge brand visibility. You subjectively pick 20 to 25 prompts that seem to show the real picture in AI search. Seem. What you get is a distorted view. When the prompt set is oversaturated with broad discovery queries, ignores product lines, leaves out local competitors, contains only brand questions and skips the other stages of the sales funnel, your AI visibility dashboard can look useful. Look. It won’t show you the priorities that actually matter.

A prompt library worth its name should help you see where your brand is present, where it’s missing, how it’s described, which sources shape the answers, which competitors the AI systems favour and what needs fixing first. The goal isn’t to track every possible prompt. It’s to build a representative sample of the AI-assisted customer journeys that matter to your business.

This guide covers the input layer of AI search measurement, the prompt set itself:

1. What is an AI prompt library?
2. The main mistakes to avoid.
3. Why “representative” doesn’t mean “exhaustive”.
4. How to decide what the library should represent.
5. How to write, group and localise prompts.
6. How to measure AI visibility in a way that holds up.
7. How to turn findings into optimisation work.
8. What the finished library looks like.
9. What’s specific to Europe: GDPR, the EU AI Act, vendor-independent tooling, localisation for Switzerland, Germany and Austria.

Prompts for AI search are often longer, more conversational and carry more constraints than a classic Google query. According to Google, queries in AI Mode are on average almost three times as long as traditional search queries, and their meaning shifts sharply with the user’s context: country, budget, product requirements, industry, buyer role, platform, urgency, decision stage. Which is why your prompt library needs a clear structure.

The main mistakes when building one:

1. Treating an AI visibility tool and its default prompt set as representative. Full stop. Tools are useful for collection, monitoring and reporting, but their default sets don’t reflect your products, audiences, markets, competitors, constraints and business priorities. Use the tool as infrastructure, then validate and customise the library before you base decisions on it. This matters most where prompts were written by marketing alone, without the goals and needs of the other departments.

2. Tracking only broad prompts. Something like “best CRM system” is useful for category-level visibility but doesn’t show the full picture.

3. Tracking only branded prompts. You need them to control accuracy and how the brand is described, but they say nothing about whether the brand is visible during discovery and selection. They also distort a dashboard fast and make it misleading. If you check visibility across all your prompts in ChatGPT, Perplexity, Claude and other models weekly, daily or monthly, branded queries will often skew the statistics. Yes, you can filter them out. Still.

4. Using one global prompt set. A prompt that’s representative in one country can be misleading in another. Countries differ in competitors, terminology, source ecosystems, marketplaces, regulation, currencies, trust signals and buyer expectations. In DACH this is sharp: the Swiss market is not the German market, and the German market is not the Austrian market, same language notwithstanding. The classic example is „Geldbörse“ versus „Portemonnaie“ (both mean wallet). The differences reach into the search results themselves. Semrush data from April and May 2025 puts AI Overview coverage at 17.5% in Portugal, 10 to 13% in Switzerland and under 1% in Germany.

5. Ignoring differences between product lines. A multi-product company can’t run one set for everything.

6. Ignoring differences between audiences. Different audiences ask different questions. Change the audience and the prompt usually has to change too.

7. Ignoring the uncertainty specific to a vertical. Finance needs trust, risk and regulatory signals. Fashion needs sizes, fit and returns. SaaS means integration, onboarding and security. Beauty means application, ingredients and so on. The library has to mirror the uncertainties users are trying to remove with AI.

8. Overreacting to a single run of a prompt. AI answers vary by platform, session, location, personalisation and time. One answer is a sample, not a fixed rank. Use repeated runs under identical conditions, and groups of prompts.

9. Blending platforms into one score. A brand can be visible in Perplexity or AI Overviews and absent from ChatGPT entirely. Platforms have to be monitored separately.

10. Writing artificial prompts. Use the real language of your audience: search data, on-site search, People Also Ask, reviews, communities, sales recordings, support tickets, CRM notes, samples of AI traffic.

11. Creating variants that don’t test a real difference. Build a variant only when it changes something meaningful: a different audience, scenario, market, competitor, constraint or stage.

12. Not connecting prompts to action. If a prompt’s result doesn’t help you diagnose, prioritise or validate something, question why it’s in the library.

13. Neglecting maintenance. Libraries aren’t static. Update them on a rhythm, keep a stable core set so the numbers stay comparable, and leave room for experimental prompts.

14. Ignoring GDPR when collecting evidence. Pasting CRM data, support tickets or customer correspondence into the library without anonymisation and a legal basis is a technical and a legal problem at once. In Europe, “evidence first” has to mean “privacy first” as well.

15. Measuring Europe as one market. Even inside DACH, coverage of AI features varies a lot. The regulatory context differs country by country, from GDPR to national rules on advertising medicines. So prompt sets, measurement protocols and conclusions all belong at market level.

Core principle: representative ≠ exhaustive

A representative library has to cover prompt groups that matter across five dimensions:

1. Customer journey stage: discovery, task solving, evaluation, comparison, validation, transaction, post-purchase.
2. Product, product line, service or category.
3. Audience or persona.
4. Market, country or language.
5. Business priority.

The library is a sampling system for the AI-assisted customer journeys you want to understand and influence.

Start with a minimum viable prompt library. Thirty to fifty commercially relevant prompts, grouped by product, line, audience, market, language and journey stage. Check that they reflect your business priorities. Add realistic buyer constraints such as budget, location, scenario, industry, integration and the urgency of the trust question. Use the audience’s actual wording.

Run that first set on the two or three platforms that matter most to you. Record the core visibility signals. Does the brand appear? Is it recommended or only mentioned? Is there a citation with a link? Which competitors show up? Which sources shape the answer? Is the description accurate?

Expand only where that first run shows real gaps.

Before the four parts in detail, here is the whole path at a glance: from raw sources to analysis, with the one stage that decides everything downstream.

FIG 01 · DATA PATH – FROM SOURCE TO PLATFORM-SEPARATED ANALYSIS

Part 1: Decide what the library should represent

Segmentation layer	Why it matters	Example
Customer journey stage	Mirrors the path from discovery to action	“Best tools for …”, “X vs. Y”, “Where can I buy …?”
Product / product line / category	Different topics, competitors, features, selection criteria	A software company tracks prompts separately for analytics, CRM and automation
Audience / persona	Different needs, language, constraints, objections	Freelancer vs. agency vs. enterprise buyer
Market / country / language	Local terminology, competitors, platforms, sources, regulation	Switzerland vs. Germany vs. Austria: different contexts, same language
Business priority	Keeps the library pointed at what earns money	Product launch, expansion market, profitable category

Business model / site type	What the library should cover
E-commerce	Product, category, comparison, attributes, price, availability, compatibility, reviews, returns, shipping, “best for …”
Marketplace	Category, seller, trustworthiness, availability, coverage, policies, comparison, location, transaction details
SaaS	Use case, industry, company size, pricing, integrations, alternatives, comparison, security, onboarding, limits
B2B services	Problem, service, expertise, industry, location, process, pricing model, references, alternatives, shortlist
Local business	“Near me”, city, district, service, opening hours, reviews, booking, emergencies, prices, trust
Travel	Destination, itinerary, hotel, transport, seasonality, budget, trip type, activities, booking
Finance	Trust, risk, supervision (BaFin/FINMA), fees, comparison, eligibility, soundness of the institution
Healthcare	Condition, treatment, provider, location, insurance, reputation, safety (Heilmittelwerbegesetz in Germany)
Publisher / media	Explainers, information, data, trends, definitions, comparisons, citation frequency
Education	Programme, degree, certification, career outcomes, cost, format, accreditation, comparison

Stage	What the user is doing	Prompt pattern
Discovery	Exploring options or a category	“What are the best tools for …”
Task solving	Looking for a solution to a problem	“How do I …”
Evaluation	Checking whether the brand fits	“Is [brand] a fit for …?”
Comparison	Weighing named options	“[Brand A] compared to [brand B] for …”
Alternatives	Looking past the option they know	“Best alternatives to …”
Shortlist	Asking what should be considered	“Which vendors should be on the shortlist for …?”
Validation	Checking trust, reviews, risk	“[Brand]: reliable? worth it?”
Transaction	Looking for where and how to buy or book	“Where to buy / book / subscribe …”
Support / post-purchase	Solving problems after the choice	“How do I return / cancel / integrate / repair …”

Each stage carries its own search intent, from open exploration to concrete intent to buy.

The commercially useful queries are often not the broadest ones. “Best CRM” is useful for category visibility, but “best CRM for a 20-person B2B team using HubSpot and Slack” mirrors the actual buying decision far better. In e-commerce: “best running shoes” is too broad; “best running shoes for beginners on wet city asphalt under €120” is useful.

Closer look: intent taxonomy for complex B2B capital equipment

With capital goods that need explaining, search intent shifts with every stage of the journey, from the open question of whether a manufacturing problem can be solved at all to the engineering and financial scrutiny of a specific quote. The deeper the buyer sits, the less marketing benefits count and the more weight goes to specifications, integration schemas (CAD/MES), total cost of ownership calculations and operating procedures. Track only “best machining centre” and you measure the first stage while missing the four where the decision is actually made.

For equipment like this, the nine journey stages of this guide condense into four macro phases: exploratory (discovery and task solving), vendor evaluation (evaluation, comparison, alternatives, shortlist), technical validation, and post-purchase (transaction and support). Each phase addresses a different role and calls for a different content type. The running example: 5-axis CNC machining centres for cutting titanium and aluminium parts (milling spindles from 20 kW), fictional, for illustration.

Exploratory: is it feasible? Role: head of manufacturing, head of technology. The brand is irrelevant at this point; what’s being searched for is the principle.

Micro scenario	Example prompt	Search driver (business risk)	Matching content type
Technological limit	“5-axis milling of titanium Ti-6Al-4V: achievable surface finish and tool life in trochoidal milling”	Scrap on an expensive blank	Cutting parameter tables (Vc, fz), tool life curves
Feasibility / performance ceiling	“Maximum material removal rate for aluminium on a 20 kW spindle”	Does the technology cover our part spectrum?	Power and removal rate diagrams
First unit costs	“Cost per part in 5-axis machining versus 3-axis setups: tooling, energy, setup time”	First case for management	Calculators for tooling, energy and setup cost

Vendor evaluation: comparing suppliers. Role: procurement, finance, management. Now it’s about risk, service and independence from the manufacturer.

Micro scenario	Example prompt	Search driver (business risk)	Matching content type
Service availability (SLA)	“Service engineer response time and spindle replacement, [manufacturer A] vs. [manufacturer B] in [region]”	Production line standstill	Service network map, SLA response times, local spare part stock
Vendor lock-in	“Can third-party tool holders and probes run on [model], or does the control lock them out?”	Monopoly pricing on consumables after purchase	Compatibility policy, open interfaces, alternatives
Total cost of ownership (5 years)	“Maintenance schedule and spare part costs over 10,000 spindle hours for [model]”	Real operating cost, not just acquisition	Replacement matrix by operating hours, with prices

Technical validation: securing compatibility. Role: engineering lead, MES/IT architecture, maintenance. What’s being checked is the fit to one specific shop floor.

Micro scenario	Example prompt	Search driver (business risk)	Matching content type
Software integration (IT/OT)	“Connecting [model] to SAP ME and Siemens Opcenter over OPC UA: tag specification”	Automating machine and operating data capture	OPC UA documentation, register tabs, ready-made connectors
Utility infrastructure	“Coolant treatment and compressed air quality requirements per ISO 8573-1 for [model]”	A failed commissioning	Infrastructure checklists, foundation and chiller requirements
Dimensions and logistics	“STEP model and foundation load plan for [model]: will the machine fit in the hall?”	Crane runway, floor load, operating zone	CAD files (STEP/DWG), drawings of the operating and loading zone

Post-purchase: running it and extending it. Role: machine operator, maintenance, process engineer. This is about downtime, servicing and expansion.

Micro scenario	Example prompt	Search driver (business risk)	Matching content type
Troubleshooting	“Chatter marks on a titanium part in 5-axis simultaneous machining [model]: causes”	A run of scrap in live production	Decision tree, corrections to feed, speed and tool overhang
Preventive maintenance (SOP)	“Interval and procedure for replacing the spindle bearing on [model]”	Swapping a wear part without wrecking the spindle	Standard operating procedures, repair kit part numbers
Retrofit	“Retrofitting a third-party pallet changer to [model]”	More output without new capital spend	Interlock signal mapping, payback calculation for the retrofit

The difference between those rows is why a single prompt like “best 5-axis machining centre” is worthless as a tracking basis: it hits none of the four roles whose questions decide the order. This level of detail belongs in the prompt matrix from step 5, one row per micro scenario, role and phase.

Product / product line	Audience	Market / language	Stage	Prompt type	Constraints
Product A	SME buyer	CH / German	Discovery	Best / shortlist	Budget, price tier
Product A	Enterprise buyer	DE / German	Comparison	Brand vs. competitor	GDPR compliance, scalability
Product B	Agency	AT / German	Evaluation	Scenario / integration	Client reporting, local terminology
Service 1	E-commerce teams	CH / German	Validation	Proof	Industry and region specific
Category 1	Consumer	DE / German	Transaction	Where to buy	Price, shipping, returns

Part 2: Build the prompt set

Constraint type	Examples (European context)
Price / budget	free, cheap, premium, up to €100, up to CHF 150, enterprise pricing
Size of the buyer	freelancer, small team, SME, mid-market (Mittelstand), large enterprise
Industry / vertical	e-commerce, medical, finance, SaaS, education, travel
Geography	Switzerland, Germany, Austria, Zurich, Munich, Vienna, DACH
Language	German (DE/AT/CH), French (CH), Italian (CH), English
Use case	reporting, booking, inventory management, lead generation, market research, compliance
Integration	Shopify, WooCommerce, WordPress, HubSpot, Salesforce, DATEV (DACH-specific for finance and accounting)
Trust / compliance	GDPR, EU hosting, ISO 27001, regulated industry, accreditation
Urgency	same day, urgent, next week
User profile	beginner, expert, family, agency, start-up, large enterprise
Preference	eco-friendly, luxury, budget, affordable, low risk, “made in EU”

A good library deliberately mixes prompts without constraints (broad category visibility) and prompts with them (realistic decision contexts).

AI Performance data in Bing Webmaster Tools

Since 10 February 2026, Microsoft has offered the AI Performance report in Bing Webmaster Tools (public preview), the first official platform data source on how Copilot and partner AI interfaces use content. ^{5 6} The metrics that matter: Total Citations, Average Cited Pages, page-level citation activity, and Grounding Queries — the key phrases the AI used when extracting content that ended up cited in AI answers. The data is a sample of total citation activity, not the full picture. ⁶ In June 2026 Microsoft added Intents (classification of grounding queries by intent: informational, commercial, research, local and so on), Topics (thematic clusters), Citation Share and Compare. ⁵

Grounding queries are not user prompts. They are search phrases the system rewrote in order to extract content. Use them to:

• see which topics and entities Microsoft’s AI systems associate with your content;
• stress-test the wording of the prompts in your library;
• find pages with high grounding activity but few visible citations.

Don’t use them as a ready-made prompt set, and don’t let them replace your audience’s real language. The limits: the report covers only the Microsoft ecosystem (Copilot, Bing, partners), shows no brand mentions in the answer text, and lists no competitors. ^{6 7}

Step 7.5: Vendor-independent tooling

The working file has to survive changes in pricing tiers, policies and AI services. Practical guidance for European companies:

• Excel-first (xlsx), compatible with LibreOffice. Much of the European mid-market runs on Excel, part of it on open-source alternatives. No macros: formulas, dropdown lists and conditional formatting only. That gets past antivirus scanners and corporate blocks on file transfer.
• Don’t build the process on a spreadsheet’s built-in AI functions. The =AI() function in Google Sheets, for instance, exists only in the paid Workspace tiers (Business Standard/Plus, Enterprise, AI Pro/Ultra), and Excel has no equivalent: Copilot can’t write results into cells as formulas. ^{2 8} Tie the library to a function like that and you’ve tied it to one tier and one vendor.
• Draft prompts through copy-paste templates that work with any LLM. Keep a sheet of prompt templates in the working file. The user copies the context (brand, market, audience, stage, constraints) plus the template and pastes both into whichever LLM the company has approved: ChatGPT, Claude, Copilot or an EU-hosted model. A human reviews the output, and only then does it enter the library. Vendor-neutral, and compatible with internal data restrictions.
• Automation inside the file rests on ordinary formulas: auto-filling market and language from the company card, calculating matrix coverage (COUNTIFS), checking for duplicate prompts (COUNTIF), flags for evidence completeness. All of it works in Excel and LibreOffice, none of it depends on an external service.

For context: Notion and Google Sheets are common tools for management libraries of reusable working prompts, and rightly so. For the measurement library the choice here is deliberately Excel, for the reasons above.

Dimension of variation	Example
Broad vs. constrained	“Best accounting software” vs. “… for freelancers in Austria under €20 a month, compatible with local requirements”
Informational vs. commercial	“How does invoice automation work?” vs. “Best invoice automation tools for small agencies”
Branded vs. non-branded	“Is [brand] a fit for agencies?” vs. “Best tools for agencies with client agreements”
One market vs. local	“Best CRM” vs. “Best CRM for German B2B SaaS start-ups with EU hosting”
One language vs. localised	German (DE) vs. Swiss German (ss instead of ß, CHF, local platforms)
Comparison vs. alternatives	“[Brand] vs. [competitor]” vs. “Best alternatives to [competitor]”
Broad audience vs. specific audience	“Best software for marketers” vs. “… for in-house SEO teams at e-commerce companies”
Feature vs. outcome	“Tools with Slack integration” vs. “Tools that alert the PR team to new news hooks”

Industry	The buyer’s uncertainty
Beauty e-commerce	Ingredients, skin type, reviews, safety, suitability, price, availability
Electronics e-commerce	Specifications, compatibility, durability, comparisons, reviews, price, warranty
Fashion e-commerce	Size, fit, material, occasion, returns, availability, style, reviews
Sport and outdoor	Fitness level, activity, terrain, weather, safety, fit of the gear
Travel	Destination, timing, itinerary, budget, availability, transport, trip type
Finance	Trust, supervision (BaFin/FINMA), fees, risk, product eligibility, creditworthiness
Healthcare	Safety, expertise, condition, location, treatment options, insurance (HWG in Germany)
SaaS	Use case, integrations, pricing, onboarding, security, GDPR, scalability
B2B services	Expertise, process, proof, industry experience, pricing model, fit
Local services	Proximity, availability, reviews, price, urgency, trust, opening hours

The GEO study (KDD 2024) also found that the effect of an optimisation tactic varies by subject area. Citing sources works best on factual questions, statistics on legal questions and opinion, expert quotes on “people and society” topics and explanatory content. There is no universal tactic, just as there is no universal prompt. ⁹

Aspect	Switzerland (CH)	Germany (DE)	Austria (AT)
Currency	CHF	EUR	EUR
Spelling	“ss” instead of “ß”	“ß”	“ß”
Marketplaces / price comparison	galaxus.ch, digitec.ch, tutti.ch, comparis.ch, toppreise.ch	Amazon.de, idealo.de, billiger.de, check24.de	geizhals.at, willhaben.at, Amazon.at
Reviews / trust	Google reviews, local directories	Trusted Shops, Trustpilot, Kununu (B2B)	Trusted Shops, local platforms
Regulation	revDSG (from 1 Sept 2023), FINMA	GDPR, BaFin, HWG (medical)	GDPR, national specifics
AI Overviews: launch languages	DE, FR, IT, EN 10	DE, EN 10	DE, EN 10
Language variants	DE / FR / IT, separate copies of the library	DE	DE

The rule is strict: one market, one language, one copy of the library. Don’t mix countries and languages in one prompt set. Create a separate copy of the working file for every market and every language version.

Localisation checklist

• Would a local user phrase this prompt exactly this way?
• Are we using the right local terminology?
• Are local competitors and constraints in there?
• Do local sources shape the answer?
• Does the prompt reflect local regulation, availability and pricing?
• Should we test in the local language, in English, or both?
• Are location modifiers needed (country, region, city)?

Part 3: Measure what you intend to defend

What to control	Recommended default
Runs per prompt	3 to 5 runs per core prompt; presence as a frequency (“appears in 4 of 5 runs”), not as a yes or no
Measurement window	The full cycle inside a short, fixed window of 24 to 72 hours
Session state	Clean, signed-out session with no history. A separate signed-in run only if personalisation itself is the object of study. EU specific: AI Overviews are shown to signed-in users aged 18 and over, so testing AIO needs a clean test account with the country setting fixed explicitly 11
Personalisation and location	Control or log location, language and account data. Set the country explicitly and record it
Platform and version	Record the platform and, where visible, the model or interface version
Collection method	Collect manually or with tools, keep the method consistent, and stay inside the platform terms. For automated collection, check the platforms’ terms of use and their data processing requirements

How to read the results:

• Presence is a frequency, not a binary.
• Separate a stable signal from noise.
• The frequency logic also filters single-run hallucinations, invented products and false attributions among them: something that appears in one of five runs is noise, not a pattern.
• Compare only what’s comparable: same platform, same session conditions, same time window.
• Adjust your collection after known changes to the platform, product or model. The AI Mode launch in the EU in October 2025 is one example: data from before and after that date isn’t comparable. ¹⁴

Three routes lead to the collection itself. Dedicated trackers such as Peec.ai or Otterly.ai automate runs, frequencies and source analysis; I use Peec in client projects myself. The manual protocol with the workbook on this page runs without any subscription and stays the reference for small libraries. Your own scripts over the APIs are the third route, in which case the platforms’ terms apply, and where personal data is involved, so does GDPR.

Hallucination or pattern? A five-step check

A single run is a sample, not a rank. That holds for a brand’s visibility exactly as it holds for an invented fact. In one answer, the two look identical. Only repetition separates the stable pattern from the random hallucination.

1. Repeat under identical conditions. Five runs minimum, seven to ten for borderline cases. Same platform, same clean signed-out session, same window of 24 to 72 hours. Only the run varies, nothing else. Any further change makes the runs incomparable.
2. Record every run in a fixed structure. Per run: does the brand appear, is it recommended, is there a citation with a link, which competitors are named, and what specific claim is made about the brand. The claim counts, not just the appearance.
3. Draw the line between frequency and noise. Count how often the mention shows up across the five runs. Four or five of five is a stable pattern, one of five is noise, two or three of five is an unstable signal, and there the only fix is more runs, not reporting. The frequency estimates the underlying probability, and a single value can’t do that.
4. Check factual accuracy separately. Frequency tells you whether a mention is stable, not whether it’s true. Three technical checks: does the cited link resolve, and does it actually support the claim? Does the claimed attribute match your primary source? Is the model confusing your brand with a namesake? An invented claim can appear stably, and then it isn’t a random hallucination but a persistent misrepresentation.
5. Classify, then act. Stable and accurate: a real visibility pattern, usable as a baseline. Stable and wrong: a persistent misrepresentation, which belongs in the entity correction work from step 19 (your own descriptions, schema markup, third-party sources), not in more runs. Unstable: more runs or a different window, no signal yet. Rare and invented: noise, ignore it, but document the model version and date, because a model change can bring it back.

If your brand appears in five of five runs and the cited link goes to your product page, that’s a pattern you can build on. If a product you never sold appears in one of five runs, with a source that leads nowhere, it’s a hallucination: note it, don’t optimise for it. The expensive mistake sits between those two, mistaking a false but stable claim for a real pattern and then working against a symptom for months instead of the cause.

Metrics: what you pin visibility to

Four KPIs come out of the protocol’s signals, and each can be reported per prompt group and per platform. Presence frequency, in how many runs the brand appears. Share of voice, the share of answers in which your brand appears against competitors. Citation rate, the share of answers with a linked citation of your domain, which is the prompt-level counterpart to Bing’s Citation Share. Description accuracy, the share of answers that describe the brand correctly. For benchmarking over time the same rule applies as everywhere in this guide: same platform, same protocol, same window.

Business complexity	Starting point
One product / one market / narrow audience	30 to 60 prompts
Multi-product site / one main market	60 to 150 prompts
SaaS or services with several audiences and competitors	100 to 250 prompts
E-commerce with several categories	150 to 500 prompts, staged by category
Marketplace with many sellers	200 to 500+ prompts by category, user type and market
International site with several focus markets	Separate market libraries, 30 to 100 prompts per market
Enterprise / multi-brand / multi-country	Modular library by brand, market, product line and stage

Goal	Recommended mix
New demand / discovery visibility	Mostly non-branded queries
Competitive positioning	Comparison and alternative prompts
Monitoring brand presence	Branded and brand-plus-scenario queries
Reputation / trust	Brand validation, reviews, risk
Product-led growth	Scenario, integrations, pricing, alternatives
E-commerce category visibility	Category, attributes, comparison, product
Local visibility	Service plus location, “near me”, reviews, availability
International expansion	Localised non-branded, competitor and market queries

Field	What it’s for
Prompt ID	Stable tracking
Prompt	The exact wording being tested
Prompt group	Analysis at topic level
Product / category	Connects visibility to a part of the business
Audience / persona	Does visibility differ by buyer type?
Journey stage	Discovery, evaluation, comparison, transaction
Prompt type	Best option, alternative, comparison, validation, local and so on
Market	Country or region (CH / DE / AT)
Language	The language being tested
Business priority	High, medium, low
Competitors included	Competitive analysis
Constraint type	Price, location, integration, compliance
Platform tested	Keeps results unmixed
Test date	AI answers vary
Run number	Interpreting repeated samples
Does the brand appear?	Prompt coverage
Is it recommended?	Frequency of recommendations
Citation with a source?	Frequency of citations
Sources cited	Analysis of the source ecosystem
Is the description accurate?	Accuracy monitoring
Possible gap	Connects presence to readiness
Next step	Turns the measurement into optimisation work
Privacy check	Anonymisation status of the evidence the prompt rests on

Bias check: 10 questions against a loaded prompt set

The most dangerous bias sits in the prompt, not in the model. A prompt that already suggests its answer measures your phrasing skill, not your visibility. Hold every prompt against these ten questions before you scale, and the bias check from the data path turns into a concrete test. A single yes on the wrong side is enough to send a row back for rework.

1. Is the brand hiding in a non-branded prompt? Does a prompt that’s meant to be open accidentally contain your brand name, or a differentiator only you have?
2. Are adjectives doing the choosing? Do words like “best”, “cheapest” or “safest” set a direction instead of asking openly for options?
3. Is the constraint cut to fit you? Is a constraint drawn so tightly that in practice only your product survives it, a niche feature nobody else offers, say?
4. Is the competitor selection fair? If you name competitors, are they the audience’s real alternatives, or hand-picked ones you look good against?
5. Is this real user language? Does the wording come from documented audience language (search data, reviews) or from your own marketing copy?
6. Does the prompt presuppose its answer? Does the question already assume the outcome, as in “why is X the best tool for …”?
7. Does the prompt only ask about strengths? A representative library also asks critically, about risks, drawbacks and reputation, not only about benefits.
8. Is a market bias hiding in there? Is the prompt quietly tailored to one market (currency, vendor, term) while looking market-neutral? That distorts every cross-market comparison.
9. Is one stage or type over-represented? Are prompts piling up in your strong stage, branded validation only for instance, so the dashboard looks better than the situation is?
10. Would a neutral third party agree? Would someone with no interest in your brand, or a competitor, accept this prompt as fair and realistic, or spot it as built in your favour?

A flattering prompt gives you a flattering dashboard, and that costs more than having none: you believe it. Representativeness isn’t a moral standard, it’s the condition under which the numbers can later carry a decision.

Part 4: Turn findings into action

Finding from the prompts	Likely cause	Next step
Brand missing in commercial queries	No decision-support content, or a weak source ecosystem	Create or improve comparison, scenario and alternative content; audit third-party sources
Brand mentioned but not recommended	Weak differentiation or weak proof	Sharper positioning, testimonials, customer references
Brand cited only via third-party sites	Your own content is harder to find	Specificity, freshness, structure, internal linking on your own pages
Brand described incorrectly	Entity inconsistency	Fix your own descriptions, schema markup, profiles and directories
Competitor wins comparison queries	The competitor has more convincing evidence	Improve comparison content and external validation
Local competitors dominate	A gap in that market’s source ecosystem	Local pages, profiles, reviews, mentions (separately for CH, DE, AT)
Product details missing	Commercial information isn’t machine-readable	Product pages, feeds, structured data, prices, availability

An evidence-based tip on prioritising (from the GEO study, KDD 2024): the most measurable effect comes from adding references to reputable sources, expert quotes and concrete statistics (up to +40% visibility; for pages outside the top positions the citation effect reached up to +115%). Excessive keyword density makes the result worse. Start your content rework with evidence density, not with rewriting keywords. ⁹

When the competitor wins: read the answer backwards

If a competitor is recommended stably across several runs and your brand isn’t, that isn’t a quirk of the output. It’s a trail. The answer reveals which sources the model pulls from and which language it rewards. You just have to read it backwards.

1. Collect stable wins, not snapshots. Take the whole prompt group where the competitor leads, several runs, several related prompts. Apply the check protocol above first: analyse only wins that hold across runs, not a single outlier.
2. Extract the cited sources and cluster them. Collect every source the winning answers name, linked directly in Perplexity and AI Overviews, and via named domains in ChatGPT and Claude. Cluster by type: the competitor’s own pages against third-party sources (comparison portals, reviews, trade press, directories, forums, Wikipedia). First question: is the competitor winning through its own content or through somebody else’s source ecosystem? The answer decides the measure.
3. Read the language patterns in the answer. Which attributes, phrasings and entities does the model attach to the competitor? Note the recurring terms: use cases, audiences, features, numbers. That’s the language the category gets described in, and the language your brand is missing from or appears differently in. Hold it against the language of your own content. Same terms? Same coupling of brand and use case?
4. Trace the claim back to its source. Open the cited third-party sources and find the sentence that carries the recommendation. Often the phrasing the model adopts sits almost verbatim in a comparison article or a review. That shows you which third-party source the model treats as an authority, and where in it your brand is missing or described more weakly.
5. Name the gap: content, entity or ecosystem. Three diagnoses, three measures. Content gap: the competitor has a comparison or use-case page and you don’t, so build it. Entity gap: the model describes your brand wrongly or incompletely, so fix your own descriptions, schema markup and profiles (step 19). Ecosystem gap: the cited third-party sources don’t know you, or barely, so build presence in exactly those sources, not in arbitrary ones.
6. Hypothesis, measure, re-measurement. Write a testable sentence, “if we’re present in [third-party source] with [attribute], our mentions in [prompt group] will rise”, implement the measure, and re-measure the same group under the same protocol after 30 days. No reverse engineering without a re-measurement, otherwise you’re guessing.

A competitor’s win is the most honest content brief you’ll get: it shows which source defines the category and which language counts there. The job then isn’t to be louder, it’s to be described more precisely in those same sources.

What gets updated	Recommended cadence
Priority commercial queries	Monthly
Branded and description prompts	Monthly
Competitive comparisons	Monthly or quarterly
Product, price and availability prompts	After material changes
Audience and persona prompts	Quarterly
Market sets (CH/DE/AT and others)	Quarterly or after market shifts
Source ecosystem map	Quarterly
Full review of the library	Every 6 to 12 months
Product or market launch prompts	Before launch and shortly after
Major platform changes (the AI Mode launch in the EU, say)	As needed, with a documented switch

Maintained like that, the library becomes working knowledge management: a documented record of how your buyers actually ask.

Role	Responsibility	Rhythm
SEO/GEO, owner of the library	Keeps the file and the change log, measures to protocol, maintains the core prompts and the source ecosystem map, prioritises gaps	Measure monthly, maintain continuously
Content team	Implements content measures (comparison and use-case pages, language from the competitor analysis), supplies real audience language from reviews and support	Per measure, contributions quarterly
Product	Supplies the facts for entity accuracy (specifications, new lines, roadmap), flags product and price changes as refresh triggers	On every change, review quarterly
Privacy / legal	Clears the legal basis for internal evidence (CRM, support) before it enters the library	On every new evidence source

The audit rhythm runs on four levels:

1. Monthly. SEO/GEO measures the core and branded prompts, updates the dashboard and records anything unusual.
2. Quarterly. A joint review by SEO, content and product: translate gaps into measures, check persona and market sets, bring the source ecosystem map up to date.
3. Half-yearly to yearly. Full audit of the whole library: representativeness and bias check across all prompts, metric definitions left untouched (change them and comparability breaks), obsolete prompts retired.
4. Event-driven. On a product or price launch, a platform change (a new AI Mode rollout, say) or a market entry: add experimental prompts and set a new baseline.

Every change gets logged with date, reason and model version. The core set stays deliberately stable so the trend comparison holds; only the experimental layer is allowed to move often (step 21). That’s what keeps management reporting readable across quarters. The most common failure in the end isn’t methodological but organisational: the library starts up, then nobody looks after it, and two quarters later the dashboard is measuring a world that no longer exists.

The workbook for this guide: three market copies to download

The complete method from this guide sits pre-wired in one Excel working file. Seven tabs walk you through the process: Business Scope as the source of truth, Audience Inputs with a privacy check per evidence row, the Prompt Matrix as a coverage map, an LLM-neutral drafting tab with five copy-paste templates, the final prompt library with automatic gates, and a verified market pack. 200 prepared evidence rows, 2,104 formulas, zero macros. Runs in Excel and LibreOffice, behind any corporate firewall.

The hardest feature is a lock. A prompt only reaches the status “Ready” once the evidence behind it has passed the privacy check, with personal data settled and a legal basis named. A red row stays out. A fictional Swiss example case is pre-filled through all the tabs, including one deliberately blocked row so you can watch the gate work.

The guide applies to all three markets. The file doesn’t: one copy per market and language, and that’s a rule rather than a convenience. Hence three downloads.

FREE · NO EMAIL GATE · COLUMN STRUCTURE STABLE ACROSS VERSIONS · VERIFIED 23 JULY 2026

Version 1.0, as of July 2026. All market packs verified against current sources on 23 July 2026. Free, no email gate. Column structure and metric names stay stable across versions, so your copies stay comparable. The workbook interface is German, because those are the markets it covers. French and Italian editions for Switzerland are in preparation; a short note to [email protected] is enough, and I prioritise by demand.

Tools to take with you

The guide doesn’t stop at instructions. This is where the finished tools collect, the ones that let you run the method directly. Extended step by step.

1. Automated runs over the APIs (Python)

An open Python script takes the finished library out of the workbook and runs it automatically over the OpenAI, Anthropic and Perplexity APIs: every prompt multiple times, platforms kept separate, with an export to Excel. It calculates presence frequency, share of voice and citation rate per prompt and platform; description accuracy you check against the raw answers it saves alongside. A dry-run mode tests the whole flow with no API keys and no cost.

An API run is a reproducible proxy, not the consumer interface: different system prompts, a different retrieval layer, no personalisation. Treat the values as an early indicator. And: transfer prompts only, never personal data. The privacy check comes first.

Download the script and instructions (.zip)

2. Interactive prompt matrix generator (no download)

Before you open the workbook you can see what a matrix looks like. Pick a market and a niche and the ten base prompts appear straight away, localised by currency, spelling and marketplace. No JavaScript at all, so it works behind corporate firewalls too.

That’s the entry point, not the replacement: the full matrix with metadata, privacy check and analysis sits in the workbook.

Example: a prompt library for Swiss e-commerce

AlpenGarten (fictional case): a Swiss online shop for premium garden furniture, delivery within Switzerland, competing with galaxus.ch, Pfister, Jumbo and specialist retailers.

1. The business question

Do AI assistants describe and recommend AlpenGarten correctly in the selection processes for premium garden furniture in Switzerland, and if not, which competitors, source ecosystems, scenarios or positioning gaps are shaping the answers in our place?

2. Extract from the matrix

• Product: premium garden furniture (lounge sets, dining sets, parasols).
• Audience: Swiss consumers (homeowners aged 35 to 65), landscape architects.
• Market/language: Switzerland / German (CH: ss, CHF).
• Stages: discovery, task solving, evaluation, comparison, alternatives, validation, transaction.
• Constraints: budget in CHF, weather resistance (alpine climate), delivery within Switzerland, materials, warranty.
• Competitors/alternatives: galaxus.ch, Pfister, Jumbo, IKEA CH, specialist retailers.
• Priority: high.

3. Prompts with real constraints (Swiss German)

These stay in German, because that’s how the buyer types them. English glosses in brackets.

• „Welche sind die besten Shops für hochwertige Gartenmöbel in der Schweiz mit Lieferung?“ [Which are the best shops for high-quality garden furniture in Switzerland with delivery?]
• „Beste wetterfeste Lounge-Garnitur für eine Terrasse in den Bergen, Budget bis CHF 3’000.“ [Best weatherproof lounge set for a terrace in the mountains, budget up to CHF 3,000.]
• „AlpenGarten vs. Galaxus für Gartenmöbel – was ist der Unterschied?“ [AlpenGarten vs. Galaxus for garden furniture: what’s the difference?]
• „Beste Alternativen zu IKEA für langlebige Gartenmöbel in der Schweiz.“ [Best alternatives to IKEA for durable garden furniture in Switzerland.]
• „Ist AlpenGarten vertrauenswürdig? Erfahrungen und Bewertungen?“ [Is AlpenGarten trustworthy? Experiences and reviews?]
• „Wo kann ich in der Schweiz Gartenmöbel aus zertifiziertem Holz kaufen?“ [Where can I buy garden furniture made from certified wood in Switzerland?]
• „Welche Gartenmöbel eignen sich für einen Balkon in Zürich bei wechselhaftem Wetter?“ [Which garden furniture suits a balcony in Zurich with changeable weather?]
• „Gartenmöbel-Shops in der Schweiz mit gutem Kundenservice und Garantie – Vergleich.“ [Garden furniture shops in Switzerland with good service and warranty: a comparison.]

4. Runs to protocol

Every core prompt: 5 runs, clean signed-out session (for AI Overviews, a clean 18+ test account with the country set to CH), a 48-hour window, platforms ChatGPT, Google AI Mode, Perplexity, Copilot. Results kept separate by platform and market.

5. One fully tagged result

Field	Example
Prompt ID	ALPG-ALT-005
Prompt	„Beste Alternativen zu IKEA für langlebige Gartenmöbel in der Schweiz“
Group	Alternatives to the mass market
Stage	Alternatives / shortlist
Audience	Swiss consumer
Market/language	CH / German (CH)
Platform	Perplexity
Runs	5
Does the brand appear?	No, 0 of 5
Recommended?	No
Competitors shown	Pfister, Jumbo, specialist retailers, Galaxus selections
Sources cited	Swiss review portals (comparis.ch), garden blogs, retail aggregators
Possible gap	AlpenGarten isn’t associated with “IKEA alternatives” or “durable Swiss garden furniture” in the cited ecosystem
Next step	Content on durability and materials versus the mass market, with statistics and citations (following the GEO study, the tactics with the largest effect) 9; presence on Swiss comparison portals; reviews on Trusted Shops and Google

6. From finding to action

The pattern: AlpenGarten shows up in branded prompts but is missing from non-branded discovery, from alternatives and from comparisons. That’s a presence gap and a readiness gap at once.

Priority measures:

• Comparison content: AlpenGarten vs. the mass market (IKEA, Jumbo) on durability, materials, warranty.
• Scenario content: furniture for the alpine climate, balcony vs. terrace vs. garden.
• Entity consistency: own pages, profiles, Swiss directories, structured data (CHF prices, delivery within Switzerland).
• Presence in the sources AI systems cite: comparis.ch, review portals, garden blogs.
• Re-run the same group under the same protocol after a month.

Short version: the process in four steps

1. Decide what the library should represent

• Business scope, business model, customer journey stages.
• Matrix: product × audience × market/language × stage × type × constraint × competitor × priority.

2. Build the prompt set

• The audience’s actual language, with a mandatory GDPR check.
• Realistic buyer constraints.
• Groups, not isolated prompts.
• Industry-specific uncertainties.
• Localisation by market, not just by language (CH ≠ DE ≠ AT).
• Deliberate competitor and alternative prompts.
• Vendor-independent tooling: Excel-first, LLM-neutral drafting.

3. Make the measurement defensible

• Metadata on every prompt, privacy check included.
• Manual validation of a small sample before scaling.
• Protocol: runs, window, session (in the EU, account the sign-in requirement for AIO), location, platform, method.
• Platforms separately, and re-check the status of features in each market, because it moves fast: AI Overviews in DACH from March 2025, AI Mode from October 2025.

4. Turn results into action

• Presence signals: appearance, recommendation, citation, sources, competitors, accuracy.
• The type of gap behind each group → measures, owners, validation.
• For content work, prioritise evidence density (citations, statistics, expert quotes) over keyword density. ⁹
• A stable core plus experimental and monitoring layers, on a refresh cadence.

Frequently asked questions

What is a prompt library? A structured, representative set of prompts you use to measure whether and how a brand appears in the answers of ChatGPT, Perplexity, Gemini, Claude and Google AI Overviews. It’s the input layer of every AI visibility measurement: what’s missing here will be missing from the dashboard later. The term covers two different things, the management library of reusable working prompts for a team and the measurement library described here for visibility tracking. The distinction is at the top of this page.

How many prompts do I need? Fewer than most people expect. Thirty to sixty is enough for one focused product in one market; the staging by business complexity is in step 14. A small, well-structured library beats a large random one.

How do I actually measure AI visibility in ChatGPT or Perplexity? Run every core prompt three to five times in a clean signed-out session, inside a window of 24 to 72 hours, and record presence as a frequency (“appears in 4 of 5 runs”), never as yes or no. Report platforms separately. The full protocol is in step 13.

What separates GEO from SEO? SEO optimises for rankings in classic search. Generative engine optimisation (GEO) optimises for being cited and recommended in the answers of generative engines. How tightly position and citation are coupled differs a lot by engine, which is why this method measures each platform separately.

Is the process GDPR-compliant? It’s built for it. Evidence from CRM, support or sales calls goes through a privacy check in the workbook (personal data, legal basis) before a prompt reaches the status “Ready”, and for sensitive industries the guide recommends EU- or Swiss-hosted models. None of that replaces legal advice.

Evidence base: studies and official sources

The material this guide rests on, and what I’d recommend for going deeper, ordered by authority:

Academic studies

1. Aggarwal et al., “GEO: Generative Engine Optimization” (Princeton, Georgia Tech, IIT Delhi, Allen Institute; KDD 2024). The foundational GEO study: a benchmark of 10,000 queries, up to +40% visibility from citations, statistics and expert quotes, and keyword stuffing performing worse than the baseline. ⁹

Official platform data (first-party)

2. Bing Webmaster Blog: AI Performance (February 2026) and the extension with Intents, Topics, Citation Share and Compare (June 2026). The only first-party reporting on AI citations. ^{5 6}
3. Google Blog: the AI Overviews launch in nine European countries (March 2025) and the AI Mode expansion (October 2025). Dates and available languages. ^{13 10}

Industry studies

4. Ahrefs: the effect of AI Overviews on click-through rate (April 2025: −34.5%; data window December 2025, published February 2026: −58%). The largest sample, 300,000 keywords, GSC data. ^{16 17}
5. Pew Research Center: user behaviour with AI Overviews (fielded March 2025, published July 2025). 8% versus 15% clicks, 1% clicks on sources. ¹
6. Semrush: AI Overview coverage by European country (May 2025). Portugal ~17.5%, Switzerland 10 to 13%, Germany under 1%. ¹²

Regulation

7. European Commission: the EU AI Act (digital-strategy.ec.europa.eu). Timeline and obligations; Art. 50 on transparency from 2 August 2026. ³
8. revDSG (Switzerland), in force since 1 September 2023; overviews from Sidley Austin and practical guides. ⁴

Tools

9. Google Workspace Updates: the =AI() function in Sheets (June 2025). Tiers and limits, plus a comparison with Excel Copilot. ^{2 8}

From visibility to revenue

Visibility isn’t an end in itself. The metrics on this page only become an argument once they connect to business figures: enquiries through the contact form, a lift in branded search, referral traffic from cited links, and deals where the client tells you ChatGPT recommended you. Ask about that in the first call, it’s the simplest attribution source there is. For reporting to management or a client, a plain frame has worked well: one metric per goal (share of voice for competitive position, citation rate for source authority, description accuracy for brand risk), separated by platform, with a month-on-month comparison.

Closing

A good AI prompt library isn’t a random list of questions. It’s a representative sampling system for the AI-assisted customer journeys you want to understand and influence. It should let you work out:

• Where your brand is present and where it’s missing.
• Whether it’s recommended, cited, or only mentioned.
• Whether it’s described accurately.
• Which competitors are favoured.
• Which sources shape the answers.
• Which gaps trace back to weak content, poor description, missing third-party validation, technical inaccessibility or the market’s source ecosystem.
• Which corrections to prioritise first.

In Europe the method carries one mandatory extra layer: privacy as part of the process (GDPR evidence from the moment of collection), market granularity (country ≠ country, even in one language, as the coverage data for AI features confirms), a regulatory horizon (EU AI Act, revDSG) and independence from tooling (the process must not collapse when the company has no access to a particular AI service or a paid tier).

The goal isn’t to capture every possible prompt. It’s to capture the right prompts well enough to make the best optimisation decisions, for the product lines, audiences, markets, platforms and journeys that matter to your business.

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Sources

1. Pew Research Center, study on click behaviour with AI Overviews (July 2025): https://odsc.medium.com/pew-study-google-users-click-less-when-ai-summaries-appear-in-search-results-cc003baf6a35 ↩ ↩²

2. Google Workspace Updates, “Generate data with Gemini in Google Sheets” (25 June 2025): https://workspaceupdates.googleblog.com/2025/06/generate-data-with-gemini-in-google-sheets.html ↩ ↩² ↩³

3. European Commission, “AI Act | Shaping Europe’s digital future”: https://digital-strategy.ec.europa.eu/en/policies/regulatory-framework-ai ↩ ↩²

4. Sidley Austin, “A Wake-Up Call: The New Swiss Data Protection Act Enters Into Force on September 1, 2023”: https://www.sidley.com/en/insights/publications/2022/04/a-wakeup-call-the-new-swiss-data-protection-act-enters-into-force-on-september-1-2023 ↩ ↩²

5. Bing Webmaster Blog, “New AI Visibility Insights in Bing Webmaster Tools: Intents, Topics, Citation Share, Compare” (16 June 2026): https://blogs.bing.com/search/June-2026/New-AI-Visibility-Insights-in-Bing-Webmaster-Tools-Intents-Topics-Citation-Share-Compare ↩ ↩² ↩³ ↩⁴

6. Bing Webmaster Blog, “Introducing AI Performance in Bing Webmaster Tools Public Preview” (10 Feb 2026): https://blogs.bing.com/webmaster/February-2026/Introducing-AI-Performance-in-Bing-Webmaster-Tools-Public-Preview ↩ ↩² ↩³ ↩⁴ ↩⁵

7. Otterly.ai, analysis of three months of AI Performance data and the limits of the report (11 Feb 2026): https://otterly.ai/blog/bing-webmaster-tools-ai-performance-report/ ↩

8. DataStudios, “The new =AI function in Google Sheets: beyond Gemini and Excel Copilot” (27 June 2025): https://www.datastudios.org/post/the-new-ai-function-in-google-sheets-beyond-gemini-and-excel-copilot ↩ ↩²

9. Aggarwal P., Murahari V., Rajpurohit T., Kalyan A., Narasimhan K., Deshpande A., “GEO: Generative Engine Optimization”, KDD 2024, arXiv:2311.09735: https://arxiv.org/pdf/2311.09735 ↩ ↩² ↩³ ↩⁴ ↩⁵

10. Google Blog, “We’re bringing the helpfulness of AI Overviews to more countries in Europe” (25 March 2025): https://blog.google/feed/were-bringing-the-helpfulness-of-ai-overviews-to-more-countries-in-europe/ ↩ ↩² ↩³ ↩⁴ ↩⁵

11. Search Engine Land, “Google rolls out AI Overviews in EU regions” (26 March 2025): https://searchengineland.com/google-rolls-out-ai-overviews-in-eu-regions-453595 ↩ ↩²

12. Semrush, “Google’s AI Overviews reach up to 17% of search results in Europe, but Germany barely breaks 1%” (6 May 2025): https://www.semrush.com/news/401232-googles-ai-overviews-reach-up-to-17-of-search-results-in-europe-but-germany-barely-breaks-1-semrush-finds/ ↩ ↩²

13. Google Blog, “AI Mode is now available in more languages and locations around the world” (7 Oct 2025): https://blog.google/products-and-platforms/products/search/ai-mode-expands-languages-locations/ ↩ ↩²

14. Cybernews, “Google AI Mode finally available across Europe” (8 Oct 2025): https://cybernews.com/ai-news/google-ai-mode-europe/ ↩ ↩²

15. Trending Topics, “Google AI Mode launches in Europe and unsettles publishers” (8 Oct 2025): https://www.trendingtopics.eu/google-ai-mode-launch-europe/ ↩

16. Ahrefs, “Update: AI Overviews Reduce Clicks by 58%” (4 Feb 2026): https://ahrefs.com/blog/ai-overviews-reduce-clicks-update/ ↩

17. Business Wire, “New Research: Google’s AI Overviews Now Cost Websites 58% of Their Clicks” (19 May 2026): https://www.businesswire.com/news/home/20260518322756/en/ ↩