How AI Search Engines Work

AI search engines are question-answering systems that use large language models (LLMs) to find information and generate responses.

There are some key differences between traditional search engines and AI search engines (although these differences are shrinking as traditional search engines incorporate more AI features):

• Instead of entering one-off queries, users can ask follow-up questions and keep the conversation going.
• Instead of returning a ranked list of links, AI search engines provide direct answers and recommendations (and these answers can change regularly).
• Instead of sending searchers to visit your website, users have their queries answered directly in the chat interface (resulting in fewer clicks back to your website).

Here’s what an archetypal AI search interface looks like, similar to what you would see in ChatGPT, Claude, or AI Mode:

LLMs are trained on huge amounts of content. They have effectively “read” all of Wikipedia, all of the Common Crawl Dataset, all of Google Books, and many millions upon millions of pages of web content.

This training data helps provide the LLM with its “understanding” of the world. If your headphone company appears many times in its training data, in relevant contexts and alongside positive descriptors (“best value”, “great for the gym”, and so on), there’s a good chance your company will be mentioned in the LLM’s responses to headphone-related prompts.

LLMs can verify and improve their answers in two ways: using tools (like calculators or other data APIs), or by retrieving additional information from external sources. This second process is technically known as Retrieval-Augmented Generation (RAG).

When a user enters a question, the LLM asks itself: “Do I already know the answer, or should I fetch additional information?” If the LLM can predict the next token with high certainty (for example, questions that don’t change much, like “what do red blood cells do?”), it’s likely to answer from its base knowledge. With low certainty (for questions that are more prone to change, like “what is the best budget coffee grinder?”), it can use its search tool to find relevant information from other sources on the internet.

The LLMs are fine-tuned to recognize query types that might benefit from additional information, like:

• Topics outside the models’ training scope: “What are the internal ranking factors used by Ahrefs’ Keywords Explorer?”
• Topics that require fresh or time-sensitive information: “What was Google’s most recent core update and when did it roll out?”
• Topics that explicitly request a web search: “Search the internet for popular link-building tactics in 2026.”
• Prompts that request sources and evidence: “Provide sources confirming Google uses user engagement signals in its algorithm.”

Some LLM models are also very likely to trigger additional searches (for example, “deep research” models are specifically configured to trigger multiple RAG searches).

Crucially, query fan-out explains why traditional SEO is critical for AI visibility.

AI assistants like ChatGPT, Gemini, and Perplexity use search indexes such as Google, Bing, and Brave to retrieve up-to-date information.

The search provider matters because each has different ranking algorithms, indexes, and coverage: making your brand visible in Google Search might help your visibility in AI Mode more than ChatGPT, which depends more heavily on Bing.

Once an LLM retrieves relevant pages from a search index, it doesn’t read them in full. Instead, pages are split into small text “chunks”, with the model prioritizing (and sometimes expanding) the sections of text that seem most relevant to the query.

These chunks are typically a few hundred to a few thousand words each, a small fraction of most web pages. The LLM also operates under strict context-window limits: it can process a limited amount of text, including the user’s prompt, all retrieved chunks, and its own response. This means it must be highly selective about what content it retrieves and includes.

Here’s an example:

This is the core of how AI search engines work, but there’s an extra level of complexity: personalization.

ChatGPT and other AI search engines can personalize their results to individual users, meaning the same prompt can generate different results for different people. Personalization can be influenced in several ways, including:

• Current conversation context: Previous messages in the same chat will influence the response to the current prompt. Mention that you value “durability” in your hiking gear, and you can expect ChatGPT to include this criterion in its search when you ask for “backpack recommendations” later in the chat.
• Memory: Many LLMs have a memory feature that lets the system retain certain facts or preferences across chats. For example, with memory enabled, ChatGPT will infer and remember details you’ve shared (like your name or interests) and include them in future conversations to personalize its responses.
• Location, time, date: Many AI search engines can infer information about you and tailor their responses with it, from using your IP address for approximate location (for queries like “brunch near me”), to the date and time (“camping packing list” might suggest a 4-season tent in Winter and a 3-season tent in Summer).
• System prompts: Any specific preferences shared in the system message will influence your conversations (adding “remember I’m vegan” to the system prompt will influence responses to prompts like “healthy breakfast ideas”).