Use Google Search Console and customer enquiries to find the questions your market already raises, then use NeuraCite to develop and test a manageable set of local GEO prompts. Google autocomplete can add useful wording and related needs. For businesses across Wales, organise the questions by service, buying decision, genuine coverage area and language. This gives you a stronger starting point than generating a long list of plausible questions without checking what makes each one relevant.
Build the prompt set in this order
Collect the words customers use when choosing a provider.
Check related Google queries and suggestions for further clues.
Add Welsh locations only where they change the buying decision.
Review generated questions, group duplicates and test a stable shortlist.
Which tools help you discover realistic local questions?
1. Google Search Console: start with existing search evidence
Search Console shows queries associated with your site's Google search performance. Review relevant service pages and look for questions about suitability, pricing, location or comparison. Some queries are omitted for privacy or reporting limitations, so treat the report as a useful sample. Google describes the query data and its limits.
2. NeuraCite: develop prompts and inspect the answers
NeuraCite supports behavioural, persona-based and Google-seeded prompt generation around a product or service. Use those suggestions as drafts for review. Its Answer Engine Insights helps test buyer questions and inspect saved answers, competitor appearances and source records.
A useful demonstration starts with your service description, customer type and genuine operating area. Check whether the resulting questions reflect how someone would choose that service. Edit anything that assumes the wrong audience, delivery model or location before it enters recurring monitoring.
3. Google autocomplete: explore adjacent wording
Autocomplete can reveal related ways people formulate a need. Google explains that suggestions draw on searches and other signals, including language, location and trends. It also says autocomplete is not simply a list of the most popular queries. Read how Google's suggestions work.
Use these sources together. They offer evidence and starting points for prompt research; they do not expose private ChatGPT conversations or establish how often a question is asked across AI assistants.
What makes a prompt specifically useful for Wales?
A Welsh prompt should represent a real service constraint or customer preference. For an on-site provider, location affects whether the business can attend. For a remote software supplier, location may matter less than billing, support, language or the ability to handle local client needs.
Keep the business's address separate from its customers' requirements. An agency based in Cardiff might serve clients across the UK. A visitor staying in Eryri might search for a nearby activity while sitting at home elsewhere. Neither use case is captured well by adding “Wales” to every generic query.
Coverage: identify towns, regions or travel distances the business actually serves.
Service: name the work or product the customer needs.
Constraint: include a genuine budget, access, delivery or scheduling requirement.
Language: include Welsh-language service where the customer needs it and the business provides it.
If Welsh and English audiences matter, maintain clearly labelled prompt groups. Have a fluent Welsh speaker review the wording and commercial meaning. Do not assume a literal translation captures the same local buying question.
How do you turn customer evidence into a prompt?
Preserve the customer's decision and remove details that are unnecessary for testing. Start with anonymised enquiry themes, sales objections, support questions and on-site searches. Record where each theme came from so the marketing team can explain why it belongs in the monitoring plan.
For example, an enquiry might ask whether an accountant can support a small company in Swansea remotely. A useful prompt could ask: “Which accountants support small businesses in Swansea online, and what should I compare before choosing one?” The important elements are the customer type, service and delivery preference.
That example illustrates the transformation; it is not a reported customer enquiry. Apply the same method to your own evidence, and avoid placing customers' names, financial details or contact information into a research prompt.
A prompt record should explain its purpose. Save the question, source of the idea, buyer stage, location, language and relevant service page. Add a brief note on what your team would change if the monitored answer exposed a gap.
How should you group and prioritise the questions?
Group prompts by the decision they represent. A provider-discovery question, a comparison question and a practical objection can justify different content. Several rewordings of the same basic request often belong in one group, especially when they would lead you to improve the same page.
The following illustrative set shows how a Cardiff marketing agency might organise research for itself:
Discovery: “Which agencies can help a small Cardiff business improve its visibility in AI search?”
Comparison: “Should a Welsh business use a GEO platform directly or work with an agency?”
Reporting: “What evidence should an agency include in an AI visibility report?”
Buying objection: “How can I evaluate a GEO service before committing to a long contract?”
Prioritise questions close to a real commercial decision and backed by customer or search evidence. Then favour gaps your team can address. A question is useful when an answer could guide a concrete improvement to a service page, case study, comparison or buying guide.
Keep speculative ideas in a separate research list. They can still be worth testing, but should not displace repeatedly observed customer needs simply because an AI generator produced more polished wording.
How do you keep monitoring comparable over time?
Maintain a stable core of prompts and a separate space for experiments. If every question changes each month, a movement in the visibility score may reflect the new sample rather than an improvement in the business's position. Version the prompt set and record important changes.
For each run, capture the engine, date, language and available location or search settings. Inspect individual answers to see whether a brand is recommended, mentioned incidentally or cited as a source. Record failed requests separately so a technical error is not mistaken for a missing recommendation.
Review the core set when the business introduces a service, changes its coverage or starts receiving a new type of enquiry. Seasonal businesses may also need temporary questions. Keep those additions labelled so a summer visitor campaign is not compared directly with an unrelated winter service mix.
What should a good prompt-research workflow deliver?
The output should be a small, explained set of buyer questions that your team can act on. Ask to see how a tool distinguishes generated ideas from underlying research, how questions can be edited and what evidence remains available after a monitoring run.
Bring one real service and anonymised enquiry themes to a NeuraCite demonstration. Compare the required prompt capacity with the current plans, then connect the resulting findings to your editorial calendar. For reporting after publication, use the guide to AI visibility, website visits and leads.