LLM Optimisation
What Is Synthetic Query Generation — And Why It Matters for eCommerce Brands
One of the biggest shifts happening under the hood of platforms like ChatGPT, Perplexity, Google, and even Amazon’s Rufus is synthetic query generation. It’s a powerful concept with real implications for brands who want to be discoverable in the new era of AI-driven search.
18 July 2025
8 min read
What is Synthetic Query Generation?
Synthetic query generation is the process of creating artificial search queries, usually by AI, to train or test search systems.
Think of it like this:
Instead of waiting for millions of people to ask real questions,
AI systems generate simulated questions based on what people might ask,
Then use these queries to test or refine the search engine's ability to deliver helpful results.
These aren’t random queries, they’re statistically likely, natural-sounding, and often tailored to a product, topic, or domain.
For example:
A brand selling magnesium supplements might see real users ask:
“Best magnesium for sleep?”
“Can I take magnesium and zinc together?”
But synthetic queries might include:
“What’s the most bioavailable magnesium supplement?”
“Magnesium glycinate vs citrate for anxiety?”
These get fed into training pipelines, allowing systems like ChatGPT or Amazon Rufus to better anticipate user intent and surface better answers.
Why Does This Matter for Brands?
Here’s the punchline: your brand’s visibility in AI systems may depend on how well you show up in synthetic queries.
AI systems are increasingly learning and ranking content based on how relevant it is to the kinds of questions users are likely to ask including the synthetic ones.
That means:
If your brand isn’t represented in content that aligns with synthetic queries, you risk being invisible.
If your content answers real and synthetic intent well, you can earn organic citations, improve LLM visibility, and potentially get recommended in AI answers.
This is the new SEO, but for LLMs.
How LMO7 Helps
At LMO7, we track how brands appear in both real-world LLM prompts and model-generated synthetic queries.
We use these to:
Identify visibility gaps
Optimise your content and Amazon listings for emerging search patterns
Recommend LLM-friendly language that mirrors synthetic query phrasing
We treat this like the new keyword research. Only smarter, faster, and more intent-driven.
TL;DR: Synthetic Query Gen in 30 Seconds
It’s how AI creates fake but realistic search questions to train and test systems.
It’s shaping what gets surfaced in tools like ChatGPT, Perplexity, and Amazon Rufus.
Brands need to align with synthetic queries to stay visible in AI-led discovery.
LMO7 helps challenger brands do exactly that.
Synthetic query generation is the process of creating artificial search queries, usually by AI, to train or test search systems.
Think of it like this:
Instead of waiting for millions of people to ask real questions,
AI systems generate simulated questions based on what people might ask,
Then use these queries to test or refine the search engine's ability to deliver helpful results.
These aren’t random queries, they’re statistically likely, natural-sounding, and often tailored to a product, topic, or domain.
For example:
A brand selling magnesium supplements might see real users ask:
“Best magnesium for sleep?”
“Can I take magnesium and zinc together?”
But synthetic queries might include:
“What’s the most bioavailable magnesium supplement?”
“Magnesium glycinate vs citrate for anxiety?”
These get fed into training pipelines, allowing systems like ChatGPT or Amazon Rufus to better anticipate user intent and surface better answers.
Why Does This Matter for Brands?
Here’s the punchline: your brand’s visibility in AI systems may depend on how well you show up in synthetic queries.
AI systems are increasingly learning and ranking content based on how relevant it is to the kinds of questions users are likely to ask including the synthetic ones.
That means:
If your brand isn’t represented in content that aligns with synthetic queries, you risk being invisible.
If your content answers real and synthetic intent well, you can earn organic citations, improve LLM visibility, and potentially get recommended in AI answers.
This is the new SEO, but for LLMs.
How LMO7 Helps
At LMO7, we track how brands appear in both real-world LLM prompts and model-generated synthetic queries.
We use these to:
Identify visibility gaps
Optimise your content and Amazon listings for emerging search patterns
Recommend LLM-friendly language that mirrors synthetic query phrasing
We treat this like the new keyword research. Only smarter, faster, and more intent-driven.
TL;DR: Synthetic Query Gen in 30 Seconds
It’s how AI creates fake but realistic search questions to train and test systems.
It’s shaping what gets surfaced in tools like ChatGPT, Perplexity, and Amazon Rufus.
Brands need to align with synthetic queries to stay visible in AI-led discovery.
LMO7 helps challenger brands do exactly that.