Most content teams have the finding part covered by now. There's a list of real buyer questions from sales calls, Search Console and Reddit, and someone picks one. Then the writer gets a single line, something like "best payroll software for restaurants," and the questions buyers ask AI after that first one never make it onto the page.
This guide covers the step in between: turning one buyer question into the brief a writer works from. If you still need the list itself, start with our guides to finding the exact prompts buyers ask ChatGPT and inferring the questions AI engines answer in your market, or our test of four question sources. Here we'll take one question all the way to a brief, using a test we ran on September 26, 2026.
Pick the question the way a buyer types it
The question needs the buyer's details in it, because that's how people write to AI search now. Semrush's clickstream panel found that ChatGPT prompts with web search on grew from 4.7 words on average in early 2025 to 8.7 words a year later.
So for our example we skipped "restaurant payroll software" and used a question an owner might type: "best payroll software for a restaurant with 15 employees." The headcount ended up shaping almost everything in the answer.
Watch what ChatGPT searches for
We sent that question through the API behind ChatGPT with web search on and logged every step it took before answering. It made six moves. It ran a broad search for restaurant payroll software, then a search for Gusto's pricing plans. It opened OnPay's pricing page and Square's payroll pricing page. Then it searched QuickBooks' site for payroll prices and ran one last search for 7shifts pricing.
Five of those six steps were about price. The answer that came back was built around the monthly cost for 15 employees: roughly $139 for Homebase and $125 for Square Payroll, with Toast on a quote. It then split its recommendation by which point-of-sale system the restaurant already runs.
That trace tells you most of what the brief needs. The model decided that a good answer has per-employee pricing for named tools and a fork for Square and Toast users, so a page without either won't match what it went looking for.
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Collect the follow-up questions
Perplexity's API returns a set of related questions with each answer. For the same query, it suggested these five:
- Which payroll software is best for handling restaurant tip reporting and tip pooling?
- What are the most affordable payroll options for a 15-employee restaurant?
- How do Paychex Flex and Restaurant365 compare for small restaurants?
- Which payroll systems integrate with popular restaurant POS platforms?
- What payroll software handles scheduling, overtime, and compliance best?
Google's People Also Ask boxes added more. For "payroll software for restaurants," they asked how to do payroll for a restaurant, what Restaurant365 costs and what software most restaurants use. For "best payroll software for small restaurant," they asked what payroll should be for a restaurant and how small businesses handle payroll.
These are the questions a reader has after the first one. A page that answers the head question and none of these sends its reader straight back to the AI to ask the rest.
Keep what two sources agree on
Not every suggested question belongs in the brief. Our filter is the two-source rule: a sub-question goes in only if at least two independent sources show buyers asking it.
Here's how the payroll questions sorted out:
| Question or theme | Google PAA | ChatGPT's searches and answer | Perplexity follow-ups | In the brief? | |
|---|---|---|---|---|---|
| Best payroll software for a small restaurant | Yes | Yes | Yes | Yes | Yes, as the head question |
| Price for 15 employees | Yes | Yes | Yes | Yes | |
| Tip reporting and tip pooling | Yes | Yes | Yes | ||
| POS integration with Toast or Square | Brand names only | Yes | Yes | Yes | Yes |
| What is the 30/30/30 rule for restaurants? | Yes | No |
Tip reporting never showed up in Google's boxes, but ChatGPT and Perplexity both raised it, so it goes in. The 30/30/30 rule went the other way. Google surfaced it for a payroll software search, but it's a rule of thumb for splitting a restaurant's revenue between food, labor and overhead, and no other source mentioned it. A writer handed that question would have spent a whole section on something no payroll buyer asked.
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Note who each engine names
Run the question in more than one engine, because they don't agree on the answer. ChatGPT's API led with Homebase, Square Payroll and Toast, and put Gusto and OnPay in a table of alternatives. Perplexity led with Gusto as the best overall pick and OnPay as the budget pick, with Square for restaurants already on Square and 7shifts for teams that want scheduling built in. Square Payroll was the only tool both engines put near the top.
For the brief, that means the comparison has to cover every tool either engine leads with, since a reader may arrive from either one with that name already in mind. It also shows you, before anyone writes a line, which rivals you're up against in each engine.
Check what's getting cited, then pick the format
The sources behind each answer tell you what shape the page should take. Nearly all of Perplexity's 15 citations were "best restaurant payroll software" roundups, from Forbes Advisor, Business.com and payroll companies' own blogs. ChatGPT's API went to vendor pricing pages for its numbers. When we asked it what owners want to know before they buy, it cited three Reddit threads and the Department of Labor's restaurant toolkit.
Put together, that points to a comparison page with prices for a stated team size, a section on tips that cites the rules, and a short part for owners already on Toast or Square. A general explainer on how payroll works would miss what both engines were reaching for. Our guide to structuring content for ChatGPT covers how to lay those sections out.
Borrow the buyers' words
The community threads also give you the wording. The restaurant threads ChatGPT cited were titled things like "which POS system and payroll provider do you use" and "best HR and payroll software recos for a small" restaurant. That phrasing belongs in your headings, since it's close to what owners will type into ChatGPT.
One marketer in an r/SEO thread about AI answers described the same shift in their own process: less time on "What should we publish?" and more on "What questions are people already asking?" Community research, they wrote, "has become the starting point for content strategy rather than keyword research alone."
Is anyone rethinking their content strategy because of AI-generated search answers?
With AI-generated search continuing to rise, I'm curious how everyone is approaching their content strategy. Are you considering building out more of an FAQ section or page, not necessarily for traditional SEO, but because LLMs seem to favo...
Write the brief
Here's the brief that came out of the payroll test. Once the research is done, it takes about five minutes to fill in.
| Brief field | Payroll example |
|---|---|
| Head question | Best payroll software for a restaurant with 15 employees |
| Reader | Owner or manager of one small restaurant, often already on a POS |
| Must answer | Monthly cost at 15 employees for each tool; tip reporting and tip pooling; which tools connect to Toast and Square |
| Proof to include | Published prices with the date they were checked; the Department of Labor's tip rules, linked |
| Format | Comparison table first, then a short section per tool, then a POS section |
| Leave out | The 30/30/30 rule and general "how payroll works" background |
| Check after publishing | Ask the head question in ChatGPT and Perplexity each month and note who gets cited |
That last row gets skipped most often. Another commenter in the same r/SEO thread said to ask ChatGPT and Gemini the questions your buyers ask, see if you show up, then check again in a month. "Otherwise you're adding FAQs on faith."
How long this takes, and the faster route
By hand, one question takes about 20 minutes. That covers a couple of Google searches, the question in ChatGPT and Perplexity, and a skim of two or three community threads. Add five minutes for the brief. A month of content built on 30 questions comes to roughly 12 hours of research before anyone writes a word.
You can keep doing it that way, and for a few cornerstone pages it's worth the time. Or RankControl can take most of it. The content engine plans titles from the questions your buyers ask and writes each article against the pages already ranking for it. AI visibility tracking then checks up to 50 of those questions every week on seven engines, including ChatGPT, Perplexity, Google AI Mode and Google AI Overviews, so the last row of the brief runs without you.
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