Definition
A synthetic query is a question a marketer writes to test AI visibility, standing in for one a real buyer might ask. Unlike a logged search term, which comes from actual demand data, a synthetic query is a hypothesis. Its value depends entirely on how closely it matches the way buyers really talk.
At a glance
- CategoriesMeasurement
- Related fieldsAnalytics, GEO
- DifficultyIntermediate
At a glance
- A synthetic query is invented, not observed, so the wording is a judgment call rather than a fact.
- Assistants expose no query data, which makes invented questions the only way to test the answer layer.
- The best sources are sales calls, support tickets and site search, because those are real sentences.
Writing the questions your buyers would type
Search gave marketers query data. Assistants give none. Nobody publishes what people ask ChatGPT about your category, and nothing in your analytics reveals it.
So you write the questions yourself, then work to guess less.
A good synthetic query is a full question rather than a phrase, because that is how people type to an assistant. It carries a constraint, because real questions do. And it uses the buyer’s words rather than yours.
“Best project management tool for a small agency that bills hourly” is a synthetic query. “Project management software” is a keyword with a question mark stapled on.
A stand-in for demand data that does not exist
The risk is not that synthetic queries are useless. It is that a set written by the marketing team quietly measures the marketing team’s assumptions.
Build your prompts from the phrases in your positioning deck and you get a list you score well on. The reports look healthy while buyers using other words never see you.
The fix is dull and works: take the sentences from people who talk to customers. Sales calls carry the exact question, the exact hedge and the exact wrong word for your category. That wrong word is what a buyer will type.
Turn real sentences into a query set
Treat the set as a working hypothesis about demand, and build it from evidence you already have.
- Pull twenty real sentences from call recordings, support tickets and your site search log. Copy them verbatim, mistakes included.
- Turn each into a full question with one constraint attached. Size, budget, industry, integration, timeline.
- Add the questions asked before yours. Buyers describe a problem before they name a category, and those problem questions are usually uncontested.
- Split the set by buyer type. Running it through persona-based querying shows which segment the wording actually reaches.
- Freeze the list and start measuring. Prompt tracking only produces a trend if the questions stop changing underneath it.
Revise the set when sales tells you the questions have changed, not when the numbers look bad.
Synthetic queries and real prompt data
| Synthetic query | Logged search data | |
|---|---|---|
| Where it comes from | Written by your team | Recorded from real users |
| Covers assistants | Yes | No |
| Main risk | Reflects your assumptions | Reflects only past behavior |
| Best use | Testing AI answer visibility | Sizing known demand |
Frequently asked questions
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Is a synthetic query just a keyword with extra words?
No. A keyword is a fragment matched against an index. A synthetic query is a complete question with a constraint in it, written the way someone speaks to an assistant. Assistants retrieve against meaning, so that constraint changes which passages come back.
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How do I know whether my queries are realistic?
Check them against recordings, not opinions. If nobody in a sales call has said the sentence, it is yours rather than a buyer’s. Read each one aloud, and rewrite anything that sounds like a headline instead of a person talking.
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How many synthetic queries do I need?
Around thirty for a first set: enough to cover category, comparison and problem-stage questions without becoming a list nobody re-runs. Depth beats volume here, so three real variations of an important question are worth more than thirty near-duplicates of an easy one you already win.
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Can synthetic queries replace real search data?
No, and treating them as equivalent is the common mistake. They are an informed guess about questions nobody can observe. Keep search data for sizing demand, use synthetic queries for testing the answer layer, and report honestly that one is measured and the other assumed.