LLM search is the process in which an answer engine such as ChatGPT, Claude, Gemini, or Perplexity gets information from many sources and combines it into one AI-generated answer. This page explains the five steps of LLM search and why they decide which brands occur in AI answers. Answer engines do not give a list of links. Thus, they answer questions in a different way from traditional search engines. When you understand this process, you understand why some brands, sources, and claims occur in AI-generated answers.

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How traditional search engines work

In traditional search engines, the interaction usually has these steps:
  1. A user enters a search query.
  2. The search engine gets pages from its index.
  3. The search engine ranks the pages by relevance and authority.
  4. The user selects one or more links.
Thus, the rank position and the click-through rate control the visibility.

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How answer engines work: the LLM search model

Answer engines have a different process. They do not give links. They make a direct answer from information that they get from many sources. This is a simplified flow:
  1. The user asks a question.
  2. The AI system expands the query into many related searches.
  3. The system gets the relevant documents and sources.
  4. The model combines the information from those sources.
  5. The model makes an answer in natural language.
  6. Some systems include citations or links to the sources.
The final answer is AI-generated. Thus, the visibility depends on whether the answer includes a brand or a source. A high rank of a web page is not sufficient.

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Step 1: The user question

The process starts when a user asks a question in natural language. Examples:
User query: What are the best running shoes for marathon training?
User query: Which CRM is best for startups?
User query: What is the best ski resort in Switzerland for beginners?
These questions are frequently longer and more conversational than the keywords of a traditional search.

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Step 2: Query expansion

Answer engines almost never use only the original question. They expand it into many related queries to get more information. These expanded searches are query fanouts. For more, read Query fanouts explained. For example, take this question:
User query: What are the best running shoes for marathon training?
Internally, the AI system can expand it into queries such as these:
  • best marathon running shoes
  • long distance running shoes reviews
  • nike vs adidas marathon shoes
  • top marathon racing shoes
With this process, the AI system examines many perspectives and gets a wider set of sources.

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Step 3: Retrieval of sources

Then the system gets information from different sources on the web. These sources can include:
  • Brand websites
  • Editorial articles
  • Product reviews
  • Comparison pages
  • Forums and communities
  • News sites
The documents that the system gets give the factual material for the final answer. For the factors that control this selection, read How LLMs select sources.

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Step 4: Synthesis

After the model gets the relevant information, it analyzes and combines that information. The model does not always quote one page. It can merge information from many sources to make a clear explanation. For example, an answer can include:
  • A list of recommended products
  • Summaries of the key advantages
  • Comparisons between alternatives
This synthesis step is one of the main differences between AI-generated answers and traditional search results.

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Step 5: Answer generation

At the end, the model makes an answer in natural language. The answer can be similar to this one:
Popular marathon running shoes include the Nike Alphafly, Adidas Adios Pro, and Saucony Endorphin Elite due to their energy return and lightweight design.
Some systems also include these items in the answer:
  • Links to sources
  • Citations
  • Suggestions for follow-up questions
  • Additional context

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Why LLM search changes brand visibility

Answer engines make an AI-generated answer. Thus, users can read only the answer and not click many links. For this reason, visibility depends on these items:
  • Whether the answer mentions a brand
  • Whether the answer cites the website of the brand
  • Whether the brand occurs in comparisons or recommendations
Thus, a brand can have strong search ranks but a small presence in AI answers. This occurs if answer engines almost never refer to the brand or connect it to the topic.

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What LLM search means for organizations

Organizations that want to occur in AI-generated answers must understand how answer engines collect and combine information. These are the important factors:
  • How clearly answer engines connect a brand to a topic
  • Whether authoritative sources refer to the brand
  • Whether the content of the brand answers usual user questions
  • How answer engines show the competitors in the same category
Answer engines make their answers from many sources. Thus, visibility depends on a wide web presence, not on one optimized page. Genezio helps teams understand how LLM search works in practice. Genezio does these tasks:
  • It runs realistic conversations with AI systems.
  • It extracts the queries that the model examines.
  • It identifies the sources that the answers use.
  • It finds the brand mentions and the citations.
  • It compares the visibility of the competitors.
With this data, organizations can see how AI systems understand their category and how their brand occurs in the answers.

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Next steps

To learn more about how AI systems get and use information, read these pages: These pages tell you how Genezio extracts structured insights from AI-generated answers.