How does ChatGPT search?

Why Understanding AI Search Matters Now More Than Ever

Search behaviour has shifted. People aren’t just typing keywords into search engines anymore; they’re asking questions directly to AI systems. That means visibility is no longer only about ranking on a results page. It’s about being selected as a source.

Understanding how ChatGPT searches is quickly becoming a practical advantage for marketers. If you know how it retrieves information, you can structure content in ways that make it easier for AI systems to find, interpret, and reference.

The biggest misconception is that AI search works like Google. It doesn’t. Traditional search retrieves pages. AI retrieves information and composes an answer.
That difference changes how content gets discovered.

What Do We Actually Mean by “ChatGPT Search”?

ChatGPT doesn’t operate as a traditional search engine. When it needs up-to-date information, it can perform searches through connected systems and retrieve results rather than relying only on stored knowledge.

Many people assume ChatGPT stores the entire internet. It doesn’t. Large language models generate responses using a combination of training data and retrieved information. When retrieval is required, they query external sources and analyse them before producing a reply.

That means finding out how ChatGPT searches is really a question about how retrieval works between your prompt and the final answer.

How Is AI Search Different From Traditional Search Engines?

Traditional search matches keywords to pages. AI search interprets intent, expands queries, retrieves multiple sources, and synthesises a response.

Google processes a query largely as text. ChatGPT processes it as meaning. If someone searches:

best CRM software

A search engine looks for pages containing those words. If someone asks:

What’s the best CRM for a small B2B company with a limited budget?

An AI model interprets context, constraints, and intent before searching.

AI retrieval focuses on:

• intent understanding

• semantic similarity

• context expansion

This is why conversational prompts change search logic. The system isn’t looking for exact phrases. It’s looking for information that satisfies the request.

What Are Fan-Out Queries?
And Why Are They So Important?

Fan-out queries are multiple related searches, generated from one prompt by an LLM, to improve coverage and accuracy in its response.

When an AI performs a web search, it doesn’t send just one query. It automatically generates several variations related to the original question.

This process happens instantly. If a user asks:

What are the top SEO strategies in 2026?

The system may internally search for:

• SEO trends 2026

• best SEO practices 2026

• search engine optimisation strategies

Each variation retrieves different sources. The model then compares them and builds a single answer.

For marketers trying to understand how ChatGPT searches, this is one of the most important mechanics to grasp. Visibility depends on appearing across a cluster of related queries, not just one keyword.

What Types of Queries Does ChatGPT Generate Behind the Scenes?

Does ChatGPT Create Synonym Variations?

AI generates semantically similar queries so it can retrieve information phrased differently.

For example, a prompt like:

top SEO strategies in 2026

might produce:

SEO trends and best practices 2026

The wording changes, but the meaning stays the same. This allows the system to find relevant content even if it doesn’t contain the original phrase.

Does It Generate Intent Variations?

AI often searches multiple intent types at once to build a complete answer.

Instead of assuming the user wants only one kind of result, the system may run searches covering:

• definitions

• comparisons

• recommendations

• examples

This helps it construct responses that address multiple angles of the same question.

Does It Generate Contextual Queries?

AI frequently adds context such as time, location, or industry, even if the user didn’t specify it.

For instance, a prompt about marketing tools could lead to searches like:

• best marketing tools for ecommerce

• marketing software trends 2026

• top platforms for B2B marketing

This contextual expansion is a core part of how ChatGPT searches effectively.

Can You See the Exact Queries ChatGPT Uses?

In some cases, generated search queries can be viewed using browser developer tools.

If a response triggers retrieval, you can inspect network requests to see which queries were sent.

Step-by-Step: How to Find ChatGPT’s Query Fan-Out –  Screenshots of these steps and make it more detailed? – Ask Tim later

1. Ask a question likely to require current information.

2. Open developer tools in your browser.

3. Go to the Network tab.

4. Reload the request.

5. Inspect query parameters.

You may see variations such as:

• “Top SEO strategies in 2026”

• “SEO trends and best practices 2026”

These reveal how the system interpreted the prompt.

For anyone studying how ChatGPT searches, this is one of the clearest ways to observe retrieval behaviour directly.

How Can You Use Fan-Out Queries for Content Strategy?

Treat each query variation as a content opportunity.

Every variation generated by AI represents a phrasing it considers relevant. That makes them useful for structuring content. You can turn them into:

• section headings

• FAQ questions

• supporting explanations

AI-generated search variations can be used directly as headings or topic sections because they reflect how retrieval systems interpret relevance.

This approach aligns your content structure with the logic used when ChatGPT searches for information. Making your content more likely to be cited.

What Are Grounding Queries — And How Are They Different?

Grounding queries are the actual searches an AI system uses to generate its answer.

Fan-out queries show predicted searches. Grounding queries show real ones. Grounding queries are the queries used during response generation.

That makes them particularly valuable. They reveal how AI actually retrieved information rather than how we think it might.

If you want to understand how ChatGPT searches in real situations, grounding data is one of the most useful signals available.

Where Can You Find Grounding Queries Today?

Some tools now expose AI retrieval data, including Bing Webmaster Tools.

Microsoft’s platform can show when your pages appear in AI responses and which queries triggered those appearances. It can track where you appeared for grounding queries used by Copilot responses.

Available metrics may include:

• citation frequency

• pages referenced

• associated queries

This data is still rare, which makes it especially valuable for understanding how ChatGPT searches and selects sources.

How Should Marketers Use Grounding Query Data?

Grounding queries function as instructions for content optimisation.

They tell you which searches already surface your pages. That means you can:

• match queries to existing pages

• add missing sections addressing them

• build new content targeting them

Grounding queries can be added as headings or used to create new pages focused on those topics.

Instead of guessing what to write, you’re responding to real retrieval behaviour.

Why Long Conversational Queries Matter More in AI Search

AI prompts tend to be longer and more conversational than traditional searches, which makes them easier for language models to interpret.

These queries often exceed eight to twelve words and resemble spoken language.

For example:

Which SEO agency can help us compete in a competitive market and drive results?

Of course, the obvious answer here is Roar Digital.

But this type of phrasing rarely appears in keyword tools, yet it reflects genuine search behaviour. Long conversational queries often signal AI-style search activity.

Because AI systems are trained on natural language, they interpret these queries more effectively than short keywords.

How Can You Find Real AI-Style Queries From Your Own Data?

Filtering your analytics for longer queries can reveal them.

One practical method is using Search Console data. Filtering queries by word count highlights conversational searches that can guide content planning.

Look for phrases that:

• sound like questions

• contain descriptive language

• resemble spoken sentences

These often represent real intent more accurately than short search terms.

What Can Query Data Tell You About Content Opportunities?

Query data shows how people actually search, revealing gaps and opportunities you might otherwise miss.

It can highlight:

• real phrasing patterns

• intent signals

• emerging topics

• areas competitors haven’t covered

For instance, the query:

Which SEO agency can help us compete in a competitive market and drive results?

is essentially a blog topic ready to write.

Building content around real queries aligns directly with how ChatGPT searches and retrieves information.

How Can You Reverse-Engineer AI Search Behaviour Strategically?

Analysing the queries AI generates, identifying patterns, and building content around them creates a reliable workflow.

Step 1 — Generate fan-out queries
Ask ChatGPT about your topic and note the variations it produces.

Step 2 — Collect grounding queries
Export data from tools that show AI citation queries.

Step 3 — Identify patterns
Look for repeated wording, shared modifiers, and recurring questions.

Step 4 — Build content around them
Create a main page, supporting sections, and FAQs that match those patterns.

This process aligns your content with the same logic used when ChatGPT searches for information.

If you’re exploring this approach but want a clearer route from insight to execution, it can help to work with a team already mapping how AI retrieval behaves across industries. GEO specialists regularly analyse real query patterns, grounding data, and citation triggers to turn this process into something structured, repeatable, and commercially focused.

What Does the Future of Search Look Like Beyond 2026?

Search is moving toward conversational discovery, where AI sits between users and information sources.

Several shifts are already visible:

• more question-based searching

• increased multi-query retrieval

• stronger emphasis on structured content

• growing importance of citation visibility

Search engines will still exist, but the interaction layer is changing. People increasingly expect answers rather than lists of links. That expectation influences how content gets discovered and selected.

Understanding how ChatGPT searches today helps prepare for how discovery will work tomorrow.

Conclusion — Your New Content Advantage

Knowing how ChatGPT searches is quickly becoming a practical skill, not a theoretical one.

Visibility is no longer just about appearing in results. It’s about being chosen as a source. That selection depends on how well your content aligns with the queries AI systems generate and retrieve.

Marketers who analyse fan-out queries, monitor grounding queries, and structure content around real conversational phrasing will be in a stronger position than those relying only on traditional keyword research.

And the more clearly you understand how AI retrieves information, the easier it becomes to make sure your content is part of the answer.