Synthetic Queries

Synthetic queries are queries generated by a search system itself, not typed by a person. When an AI search system such as AI Mode, Perplexity or ChatGPT search receives a question, it treats it as a starting signal, then generates further queries to interpret, expand and explore the topic before writing an answer. Users never see these queries, yet they decide which documents are retrieved and which sources get cited. MLforSEO puts it simply: modern SEO increasingly involves competing in searches no human ever typed.

How it works

Synthetic queries are the mechanism behind query fan-out and an evolution of query augmentation. Systems generate them for a few reasons:

• Ambiguity: “python tutorial” could mean the language, the snake or Monty Python, so the system tests several interpretations.
• Semantic coverage: informal phrasing (“why does my chest hurt when I breathe”) is expanded into the vocabulary documents actually use.
• Entity understanding: a query about a person or company is expanded to related entities, roles and attributes.
• Multi-dimensional questions: “best city to live in 2026” implies sub-questions about cost of living, jobs, safety and schools.

Google’s patents describe this directly, including thematic search with sub-themes, generating query variants with trained generative models, and prompt-based query generation, where a language model writes synthetic query and document pairs that are then used to train a retrieval model. User context can shape which sub-queries are generated, so the same question can fan out differently for different people.

The term has a second, data-quality meaning in MLforSEO’s course: queries in your own datasets that were produced by automated tools (rank trackers, SERP monitors) rather than real users. Separating these from user-initiated queries is part of cleaning keyword and Search Console data.

How it’s used in SEO and marketing

• Simulating likely fan-out around a target topic and checking whether your content answers each sub-question.
• Measuring semantic coverage instead of keyword density.
• Classifying synthetic versus user queries in datasets, with rule-based and machine learning detection.
• Planning entity and attribute coverage using the EAV model, since many synthetic queries are entity-attribute combinations.

Example

For “best running shoes for flat feet”, a system may generate sub-queries about arch support, pronation control, stability versus motion-control shoes and podiatrist recommendations. A page that only targets the head term may miss all of them, while a page with clear sections on each is retrievable several times over.

Limitations and things to watch

• You cannot see the real synthetic queries a system ran; any simulation is an approximation.
• Fan-out varies by user, context and over time, so treat it as a coverage guide rather than a keyword list.
• Patents show what a company has explored, not necessarily what is in production today.

Learn it on MLforSEO

• What Are Synthetic Queries? Why They Matter for Semantic SEO and AI Search
• How Entities Power Query Fan-Out in AI Search
• Synthetic vs User Query Classification Notebook
• Semantic ML-enabled Keyword Research, bonus module on queries in the AI era