AI Search: how AI-powered search works

As of: 3 November 2026.

AI Search is search in which an AI model finds pages and composes an answer to the user's question from them. The system splits a complex question into sub-queries, looks up information in many sources and synthesizes it with links. This group includes Google AI Overviews, Google AI Mode, ChatGPT Search and Perplexity, among others. Classic search still runs in the background, because AI relies on indexes and page content. What changes is mainly the form in which the user gets the result.

Which tools belong to AI Search?

AI Search is the name of a category of solutions. What they share is that the search engine finds documents and helps interpret the information in them.

In a traditional search engine the user gets a ranking of pages. Google results have long included answer snippets, knowledge panels, local results and video too. Even so, it is usually the user who has to open several pages, compare the data and draw a conclusion.

In AI Search the system takes over part of that work. It looks up information, puts it together and prepares an answer tailored to the question.

AI Mode reached Poland on 8 October 2025. benchmark.pl reports that Google then expanded it to more than 40 new countries and 38 languages, including Polish. At launch the mode ran on the Gemini 2.5 model and accepted questions by text, voice or an image from the phone camera.

How does AI search work?

AI search works in four steps: understanding the question, splitting it into sub-queries, retrieving and evaluating content, and synthesizing the answer. An example from Google AI Mode shows this well.

The user types: “I'm looking for a light laptop for working while travelling, up to $1,000. I care about long battery life, a screen of at least 14 inches and photo editing.” That is several conditions at once: price, weight, battery, screen and performance.

• Query: the system analyzes the question and tries to establish its intent.
• Sub-queries: it splits the problem into smaller parts and searches for each separately.
• Retrieval and evaluation: it finds material for each part and assesses how useful it is.
• Synthesis: a generative model combines the information into an answer with links to sources.

The second step is called query fan-out. Google's documentation on AI features in Search describes it as issuing multiple related searches across subtopics and data sources. This lets the answer show a wider and more diverse set of links. In the laptop example, separate sub-queries may cover price, battery, weight, screen, performance and available models.

Semantic search plays an important role. It finds content with a similar meaning even without identical words. “Phone with a good battery” and “smartphone that lasts long without charging” describe the same need.

One technique behind this kind of search is embeddings. Text is turned into a vector of numbers, and similar content sits close together in vector space. Similarity is measured with cosine similarity, among other methods. Google does not publish the full search architecture of AI Mode, so this describes the general mechanism. Google's algorithm may work differently.

The synthesis resembles RAG, or retrieval-augmented generation. Patrick Lewis and colleagues described RAG in 2020 as a general method that connects a language model to a searchable knowledge index (Wikipedia in the study). AI Search applies it to content from the web.

How do AI Overviews and AI Mode differ?

AI Overviews is a short summary within classic results, while AI Mode is a separate mode for conversing with the search engine. Both Google features use generative AI, but they do different jobs. According to Google's documentation they may also use different models and techniques, so their answers and links differ.

In AI Mode the user can narrow the topic without retyping the question. After the first answer it is enough to ask: “Which option will be cheapest?” or “Compare only the first two.”

The line between the two features is gradually blurring. On mobile, Google already lets users ask a follow-up straight from the summary and continue the conversation in AI Mode with the context of the first search.

ChatGPT Search works in a similar way. When a question needs current data, ChatGPT searches the web and cites its sources. Conversation context matters a lot, because a follow-up question does not require describing the problem again.

What does AI Search change for users and websites?

AI Search changes how people ask questions and reduces clicks on classic results. Instead of a few keywords, the user describes the whole problem in plain language. Nikola Todorovic, director of software engineering at Google Search, spoke about queries becoming more conversational and complex in an episode of the Search Off the Record podcast.

Instead of “laptop work $1000” you can ask: “Which light laptop under $1,000 with a good battery should I choose for working while travelling?” You can also follow up without starting over.

For site owners this is a new situation. A page can be the source of an answer and still not get the visit. A Pew Research Center study covered browsing data from 900 US adults in March 2025.

Fewer clicks do not settle the question of worse results. Someone who visits after reading the summary already knows the basics, so the quality of these visits is worth assessing separately.

Measure the effect of AI Search more broadly than by visits. Conversions count, as do visit quality and duration, traffic coming straight from AI tools and brand visibility in cited sources. Traffic from ChatGPT or Perplexity usually shows up in GA4 as referrals from their domains (e.g. chatgpt.com, perplexity.ai), though some visits, for example from mobile apps, land in Direct. Whether the measurement itself works properly is what the free GA4 audit checks.

Query fan-out also changes the rules of competition. Content does not have to win the whole query. It is enough to answer one part of it best and be easy to cite. How this affects source selection is covered in our piece on duplicate content and AI citations. Writing for such answers is now part of SEO at Zest.